Collaborative geo-positioning of electronic devices

ABSTRACT

Methods and systems for determining and tracking electronic devices are provided. One method includes obtaining measurement data from each of at least a subset of a plurality of client devices and determining, by a computer processing device, geolocation estimation data for one or more of the subset of the plurality of client devices based at least in part on the measurement data. The method further determining, by the computer processing device, a geolocation confidence value based at least in part on the geolocation estimation data, wherein the geolocation confidence value indicates a level of confidence of the geolocation estimation data. The method further includes providing the geolocation estimation data and the geolocation confidence value to the one or more of the subset of the plurality of client devices.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/925,219, filed Mar. 19, 2018, which claims the benefit of U.S.Provisional Patent Application No. 62/473,726, filed Mar. 20, 2017, theentire contents of each of which are hereby incorporated by reference.

BACKGROUND

This specification relates to a data communication system and, inparticular, systems and methods for determining and tracking theapproximate geolocation of electronic devices.

The publish-subscribe (or “PubSub”) pattern is a data communicationmessaging arrangement implemented by software systems where so-calledpublishers publish messages to topics and so-called subscribers receivethe messages pertaining to particular topics to which they aresubscribed. There can be one or more publishers per topic and publishersgenerally have no knowledge of what subscribers, if any, will receivethe published messages. Because publishers may publish large volumes ofmessages, and subscribers may subscribe to many topics (or “channels”)the overall volume of messages directed to a particular channel and/orsubscriber may be difficult to manage.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates an example system that supports the PubSubcommunication pattern.

FIG. 1B illustrates functional layers of software on an example clientdevice.

FIG. 2 is a diagram of an example messaging system.

FIG. 3A is a data flow diagram of an example method for writing data toa streamlet.

FIG. 3B is a data flow diagram of an example method for reading datafrom a streamlet.

FIG. 4A is a data flow diagram of an example method for publishingmessages to a channel of a messaging system.

FIG. 4B is a data flow diagram of an example method for subscribing to achannel of a messaging system.

FIG. 4C is an example data structure for storing messages of a channelof a messaging system.

FIG. 5A is a data flow diagram of an example method for publishing andreplicating messages of a messaging system.

FIG. 5B is a data flow diagram of an example method for retrievingstored messages in a messaging system.

FIGS. 5C and 5D are data flow diagrams of example methods for repairinga chain of copies of data in a messaging system.

FIG. 6 is an example data flow diagram for the application of filteringcriteria in a messaging system.

FIGS. 7A-7D are illustrations of how messages may be processed usingquery instructions that include a period-based parameter.

FIG. 8 is a diagram illustrating an example map of an environment inwhich one or more electronic devices may be determined and tracked.

FIG. 9 is a diagram of an example system architecture that may be usedto track and locate electronic devices in an environment.

FIG. 10 is a flowchart of an example method for determining and trackingthe approximate geolocation of electronic devices in an environment.

FIG. 11 is a flowchart of an example method for calculating geolocationestimation data and for calculating a geolocation confidence value.

FIG. 12 is a flowchart of an example method for determining and trackingthe approximate geolocation of electronic devices in an environment.

FIG. 13 is a block diagram of an example computing device that mayperform one or more of the operations described herein, in accordancewith the present embodiments.

DETAILED DESCRIPTION

Elements of examples or embodiments described with respect to a givenaspect of the invention can be used in various embodiments of anotheraspect of the invention. For example, it is contemplated that featuresof dependent claims depending from one independent claim can be used inapparatus, systems, and/or methods of any of the other independentclaims.

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

A system architecture for locating and tracking electronic devices inindoor and outdoor environments may include a messaging system. Themessaging system may support the PubSub communication pattern and mayallow publishers and subscribers to publish and receive live messages.Users of certain electronic devices may want to determine and trackother electronic devices, and thus may include both publishers andsubscribers of the messaging system. Electronic devices may publishmessages that indicate approximate geolocations of the electronicdevices. Users may view the messages and corresponding approximategeolocations of the electronic devices as the users of the electronicdevices move within or about indoor or outdoor environments.

For example, the present embodiments may be directed to a schema forcollaborative geo-positioning of multiple electronic devices that mayimprove triangulation accuracy and precision of locating one or moreelectronic devices of a number of electronic devices by calculating andrecording information that may be used to improve accuracy and precisionof future geolocation estimates. The present techniques also minimizethe amount of information stored on servers that could be used to trackand locate devices in indoor and outdoor environments, or even worldwidein some examples. In this way, the present embodiments may providetechniques to efficiently determine and track the approximategeolocation of electronic devices within or about indoor and outdoorenvironments, or otherwise in any of various environments in whichlarge-scale satellite systems may be inaccurate, imprecise, or otherwiseunavailable.

FIG. 1A illustrates an example system 100 that supports the PubSubcommunication pattern. Publisher clients (e.g., Publisher 1) can publishmessages to named channels (e.g., “Channel 1”) by way of the system 100.A message can comprise any type of information including one or more ofthe following: text, image content, sound content, multimedia content,video content, binary data, and so on. Other types of message data arepossible. Subscriber clients (e.g., Subscriber 2) can subscribe to anamed channel using the system 100 and start receiving messages whichoccur after the subscription request or from a given position (e.g., amessage number or time offset). A client can be both a publisher and asubscriber.

Depending on the configuration, a PubSub system can be categorized asfollows:

-   -   One to One (1:1). In this configuration there is one publisher        and one subscriber per channel. A typical use case is private        messaging.    -   One to Many (1:N). In this configuration there is one publisher        and multiple subscribers per channel. Typical use cases are        broadcasting messages (e.g., stock prices).    -   Many to Many (M:N). In this configuration there are many        publishers publishing to a single channel. The messages are then        delivered to multiple subscribers. Typical use cases are map        applications.

There is no separate operation needed to create a named channel. Achannel is created implicitly when the channel is subscribed to or whena message is published to the channel. In some implementations, channelnames can be qualified by a name space. A name space comprises one ormore channel names. Different name spaces can have the same channelnames without causing ambiguity. The name space name can be a prefix ofa channel name where the name space and channel name are separated by adot or other suitable separator. In some implementations, name spacescan be used when specifying channel authorization settings. Forinstance, the messaging system 100 may have app1.foo andapp1.system.notifications channels where “app1” is the name of the namespace. The system can allow clients to subscribe and publish to theapp1.foo channel. However, clients can only subscribe to, but notpublish to the app1.system.notifications channel.

FIG. 1B illustrates functional layers of software on an example clientdevice. A client device (e.g., client 102) is a data processingapparatus such as, for example, a personal computer, a laptop computer,a tablet computer, a smart phone, a smart watch, or a server computer.Other types of client devices are possible. The application layer 104comprises the end-user application(s) that will integrate with thePubSub system 100. The messaging layer 106 is a programmatic interfacefor the application layer 104 to utilize services of the system 100 suchas channel subscription, message publication, message retrieval, userauthentication, and user authorization. In some implementations, themessages passed to and from the messaging layer 106 are encoded asJavaScript Object Notation (JSON) objects. Other message encodingschemes are possible.

The operating system 108 layer comprises the operating system softwareon the client 102. In various implementations, messages can be sent andreceived to/from the system 100 using persistent or non-persistentconnections. Persistent connections can be created using, for example,network sockets. A transport protocol such as TCP/IP layer 112implements the Transport Control Protocol/Internet Protocolcommunication with the system 100 that can be used by the messaginglayer 106 to send messages over connections to the system 100. Othercommunication protocols are possible including, for example, UserDatagram Protocol (UDP). In further implementations, an optionalTransport Layer Security (TLS) layer 110 can be employed to ensure theconfidentiality of the messages.

FIG. 2 is a diagram of an example messaging system 100. The system 100provides functionality for implementing PubSub communication patterns.The system comprises software components and storage that can bedeployed at one or more data centers 122 in one or more geographiclocations, for example. The system comprises MX nodes (e.g., MX nodes ormultiplexer nodes 202, 204 and 206), Q nodes (e.g., Q nodes or queuenodes 208, 210 and 212), one or more configuration manager nodes (e.g.,configuration manager 214), and optionally one or more C nodes (e.g., Cnodes or cache nodes 220 and 222). Each node can execute in a virtualmachine or on a physical machine (e.g., a data processing apparatus).Each MX node can serve as a termination point for one or more publisherand/or subscriber connections through the external network 216. Theinternal communication among MX nodes, Q nodes, C nodes, and theconfiguration manager can be conducted over an internal network 218, forexample. By way of illustration, MX node 204 can be the terminus of asubscriber connection from client 102. Each Q node buffers channel datafor consumption by the MX nodes. An ordered sequence of messagespublished to a channel is a logical channel stream. For example, ifthree clients publish messages to a given channel, the combined messagespublished by the clients comprise a channel stream. Messages can beordered in a channel stream, for example, by time of publication by theclient, by time of receipt by an MX node, or by time of receipt by a Qnode. Other ways for ordering messages in a channel stream are possible.In the case where more than one message would be assigned to the sameposition in the order, one of the messages can be chosen (e.g.,randomly) to have a later sequence in the order. Each configurationmanager node is responsible for managing Q node load, for example, byassigning channels to Q nodes and/or splitting channel streams intoso-called streamlets. Streamlets are discussed further below. Theoptional C nodes provide caching and load removal from the Q nodes.

In the example messaging system 100, one or more client devices(publishers and/or subscribers) establish respective persistentconnections (e.g., TCP connections) to an MX node (e.g., MX node 204).The MX node serves as a termination point for these connections. Forinstance, external messages (e.g., between respective client devices andthe MX node) carried by these connections can be encoded based on anexternal protocol (e.g., JSON). The MX node terminates the externalprotocol and translates the external messages to internal communication,and vice versa. The MX nodes publish and subscribe to streamlets onbehalf of clients. In this way, an MX node can multiplex and mergerequests of client devices subscribing for or publishing to the samechannel, thus representing multiple client devices as one, instead ofone by one.

In the example messaging system 100, a Q node (e.g., Q node 208) canstore one or more streamlets of one or more channel streams. A streamletis a data buffer for a portion of a channel stream. A streamlet willclose to writing when its storage is full. A streamlet will close toreading and writing and be de-allocated when its time-to-live (TTL) hasexpired. By way of illustration, a streamlet can have a maximum size of1 MB and a TTL of three minutes. Different channels can have streamletslimited by different sizes and/or by different TTLs. For example,streamlets in one channel can exist for up to three minutes, whilestreamlets in another channel can exist for up to 10 minutes. In variousimplementations, a streamlet corresponds to a computing process runningon a Q node. The computing process can be terminated after thestreamlet's TTL has expired, thus freeing up computing resources (forthe streamlet) back to the Q node, for example.

When receiving a publish request from a client device, an MX node (e.g.,MX node 204) makes a request to a configuration manager (e.g.,configuration manager 214) to grant access to a streamlet to write themessage being published. Note, however, that if the MX node has alreadybeen granted write access to a streamlet for the channel (and thechannel has not been closed to writing), the MX node can write themessage to that streamlet without having to request a grant to accessthe streamlet. Once a message is written to a streamlet for a channel,the message can be read by MX nodes and provided to subscribers of thatchannel.

Similarly, when receiving a channel subscription request from a clientdevice, an MX node makes a request to a configuration manager to grantaccess to a streamlet for the channel from which messages are read. Ifthe MX node has already been granted read access to a streamlet for thechannel (and the channel's TTL has not been closed to reading), the MXnode can read messages from the streamlet without having to request agrant to access the streamlet. The read messages can then be forwardedto client devices that have subscribed to the channel. In variousimplementations, messages read from streamlets are cached by MX nodes sothat MX nodes can reduce the number of times needed to read from thestreamlets.

By way of illustration, an MX node can request a grant from theconfiguration manager that allows the MX node to store a block of datainto a streamlet on a particular Q node that stores streamlets of theparticular channel. Example streamlet grant request and grant datastructures are as follows:

StreamletGrantRequest = { “channel”: string( ) “mode”: “read” | “write”“position”: 0 } StreamletGrantResponse = { “streamlet-id”:“abcdef82734987”, “limit-size”: 2000000, # 2 megabytes max “limit-msgs”:5000, # 5 thousand messages max “limit-life”: 4000, # the grant is validfor 4 seconds “q-node”: string( ) “position”: 0 }

The StreamletGrantRequest data structure stores the name of the streamchannel and a mode indicating whether the MX node intends on readingfrom or writing to the streamlet. The MX node sends theStreamletGrantRequest to a configuration manager node. The configurationmanager node, in response, sends the MX node a StreamletGrantResponsedata structure. The StreamletGrantResponse contains an identifier of thestreamlet (streamlet-id), the maximum size of the streamlet(limit-size), the maximum number of messages that the streamlet canstore (limit-msgs), the TTL (limit-life), and an identifier of a Q node(q-node) on which the streamlet resides. The StreamletGrantRequest andStreamletGrantResponse can also have a position field that points to aposition in a streamlet (or a position in a channel) for reading fromthe streamlet.

A grant becomes invalid once the streamlet has closed. For example, astreamlet is closed to reading and writing once the streamlet's TTL hasexpired and a streamlet is closed to writing when the streamlet'sstorage is full. When a grant becomes invalid, the MX node can request anew grant from the configuration manager to read from or write to astreamlet. The new grant will reference a different streamlet and willrefer to the same or a different Q node depending on where the newstreamlet resides.

FIG. 3A is a data flow diagram of an example method for writing data toa streamlet in various embodiments. In FIG. 3A, when an MX node (e.g.,MX node 202) request to write to a streamlet is granted by aconfiguration manager (e.g., configuration manager 214), as describedbefore, the MX node establishes a Transmission Control Protocol (TCP)connection with the Q node (e.g., Q node 208) identified in the grantresponse received from the configuration manager (302). A streamlet canbe written concurrently by multiple write grants (e.g., for messagespublished by multiple publisher clients). Other types of connectionprotocols between the MX node and the Q node are possible.

The MX node then sends a prepare-publish message with an identifier of astreamlet that the MX node wants to write to the Q node (304). Thestreamlet identifier and Q node identifier can be provided by theconfiguration manager in the write grant as described earlier. The Qnode hands over the message to a handler process 301 (e.g., a computingprocess running on the Q node) for the identified streamlet (306). Thehandler process can send to the MX node an acknowledgement (308). Afterreceiving the acknowledgement, the MX node starts writing (publishing)messages (e.g., 310, 312, 314, and 318) to the handler process, which inturn stores the received data in the identified streamlet. The handlerprocess can also send acknowledgements (316, 320) to the MX node for thereceived data. In some implementations, acknowledgements can bepiggy-backed or cumulative. For example, the handler process can send tothe MX node an acknowledgement for each predetermined amount of datareceived (e.g., for every 100 messages received) or for everypredetermined time period (e.g., for every one millisecond). Otheracknowledgement scheduling algorithms, such as Nagle's algorithm, can beused.

If the streamlet can no longer accept published data (e.g., when thestreamlet is full), the handler process sends a Negative-Acknowledgement(NAK) message (330) indicating a problem, following by an EOF(end-of-file) message (332). In this way, the handler process closes theassociation with the MX node for the publish grant. The MX node can thenrequest a write grant for another streamlet from a configuration managerif the MX node has additional messages to store.

FIG. 3B is a data flow diagram of an example method for reading datafrom a streamlet in various embodiments. In FIG. 3B, an MX node (e.g.,MX node 204) sends to a configuration manager (e.g., configurationmanager 214) a request for reading a particular channel starting from aparticular message or time offset in the channel. The configurationmanager returns to the MX node a read grant including an identifier of astreamlet containing the particular message, a position in the streamletcorresponding to the particular message, and an identifier of a Q node(e.g., Q node 208) containing the particular streamlet. The MX node thenestablishes a TCP connection with the Q node (352). Other types ofconnection protocols between the MX node and the Q node are possible.

The MX node then sends to the Q node a subscribe message (354) with theidentifier of the streamlet (in the Q node) and the position in thestreamlet from which the MX node wants to read (356). The Q node handsover the subscribe message to a handler process 351 for the streamlet(356). The handler process can send to the MX node an acknowledgement(358). The handler process then sends messages (360, 364, 366), startingat the position in the streamlet, to the MX node. In someimplementations, the handler process can send all of the messages in thestreamlet to the MX node. After sending the last message in a particularstreamlet, the handler process can send a notification of the lastmessage to the MX node. The MX node can send to the configurationmanager another request for another streamlet containing a next messagein the particular channel.

If the particular streamlet is closed (e.g., after its TTL has expired),the handler process can send an unsubscribe message (390), followed byan EOF message (392), to close the association with the MX node for theread grant. The MX node can close the association with the handlerprocess when the MX node moves to another streamlet for messages in theparticular channel (e.g., as instructed by the configuration manager).The MX node can also close the association with the handler process ifthe MX node receives an unsubscribe message from a corresponding clientdevice.

In various implementations, a streamlet can be written into and readfrom at the same time instance. For example, there can be a valid readgrant and a valid write grant at the same time instance. In variousimplementations, a streamlet can be read concurrently by multiple readgrants (e.g., for channels subscribed to by multiple publisher clients).The handler process of the streamlet can order messages from concurrentwrite grants based on, for example, time-of-arrival, and store themessages based on the order. In this way, messages published to achannel from multiple publisher clients can be serialized and stored ina streamlet of the channel.

In the messaging system 100, one or more C nodes (e.g., C node 220) canoffload data transfers from one or more Q nodes. For instance, if thereare many MX nodes requesting streamlets from Q nodes for a particularchannel, the streamlets can be offloaded and cached in one or more Cnodes. The MX nodes (e.g., as instructed by read grants from aconfiguration manager) can read the streamlets from the C nodes instead.

As described above, messages for a channel in the messaging system 100are ordered in a channel stream. A configuration manager (e.g.,configuration manager 214) splits the channel stream into fixed-sizedstreamlets that each reside on a respective Q node. In this way, storinga channel stream can be shared among many Q nodes; each Q node stores aportion (one or more streamlets) of the channel stream. Moreparticularly, a streamlet can be stored in, for example, registersand/or dynamic memory elements associated with a computing process on aQ node, thus avoiding the need to access persistent, slower storagedevices such as hard disks. This results in faster message access. Theconfiguration manager can also balance load among Q nodes in themessaging system 100 by monitoring respective workloads of the Q nodesand allocating streamlets in a way that avoids overloading any one Qnode.

In various implementations, a configuration manager maintains a listidentifying each active streamlet, the respective Q node on which thestreamlet resides, an identification of the position of the firstmessage in the streamlet, and whether the streamlet is closed forwriting. In some implementations, Q nodes notify the configurationmanager and/or any MX nodes that are publishing to a streamlet that thestreamlet is closed due to being full or when the streamlet's TTL hasexpired. When a streamlet is closed, the streamlet remains on theconfiguration manager's list of active streamlets until the streamlet'sTTL has expired so that MX nodes can continue to retrieve messages fromthe streamlet.

When an MX node requests a write grant for a given channel and there isnot a streamlet for the channel that can be written to, theconfiguration manager allocates a new streamlet on one of the Q nodesand returns the identity of the streamlet and the Q node in theStreamletGrantResponse. Otherwise, the configuration manager returns theidentity of the currently open for writing streamlet and corresponding Qnode in the StreamletGrantResponse. MX nodes can publish messages to thestreamlet until the streamlet is full or the streamlet's TTL hasexpired, after which a new streamlet can be allocated by theconfiguration manager.

When an MX node requests a read grant for a given channel and there isnot a streamlet for the channel that can be read from, the configurationmanager allocates a new streamlet on one of the Q nodes and returns theidentity of the streamlet and the Q node in the StreamletGrantResponse.Otherwise, the configuration manager returns the identity of thestreamlet and Q node that contains the position from which the MX nodewishes to read. The Q node can then begin sending messages to the MXnode from the streamlet beginning at the specified position until thereare no more messages in the streamlet to send. When a new message ispublished to a streamlet, MX nodes that have subscribed to thatstreamlet will receive the new message. If a streamlet's TTL hasexpired, the handler process 351 can send an EOF message (392) to any MXnodes that are subscribed to the streamlet.

In some implementations, the messaging system 100 can include multipleconfiguration managers (e.g., configuration manager 214 plus one or moreother configuration managers). Multiple configuration managers canprovide resiliency and prevent single point of failure. For instance,one configuration manager can replicate lists of streamlets and currentgrants it maintains to another “slave” configuration manager. As anotherexample, multiple configuration managers can coordinate operationsbetween them using distributed consensus protocols, such as, forexample, Paxos or Raft protocols.

FIG. 4A is a data flow diagram of an example method for publishingmessages to a channel of a messaging system. In FIG. 4A, publishers(e.g., publisher clients 402, 404, 406) publish messages to themessaging system 100 described earlier in reference to FIG. 2. Forinstance, publishers 402 respectively establish connections 411 and sendpublish requests to the MX node 202. Publishers 404 respectivelyestablish connections 413 and send publish requests to the MX node 206.Publishers 406 respectively establish connections 415 and send publishrequests to the MX node 204. Here, the MX nodes can communicate (417)with a configuration manager (e.g., configuration manager 214) and oneor more Q nodes (e.g., Q nodes 212 and 208) in the messaging system 100via the internal network 218.

By way of illustration, each publish request (e.g., in JSON key/valuepairs) from a publisher to an MX node includes a channel name and amessage. The MX node (e.g., MX node 202) can assign the message in thepublish request to a distinct channel in the messaging system 100 basedon the channel name (e.g., “foo”) of the publish request. The MX nodecan confirm the assigned channel with the configuration manager 214. Ifthe channel (specified in the subscribe request) does not yet exist inthe messaging system 100, the configuration manager can create andmaintain a new channel in the messaging system 100. For instance, theconfiguration manager can maintain a new channel by maintaining a listidentifying each active streamlet of the channel's stream, therespective Q node on which the streamlet resides, and identification ofthe positions of the first and last messages in the streamlet asdescribed earlier.

For messages of a particular channel, the MX node can store the messagesin one or more buffers or streamlets in the messaging system 100. Forinstance, the MX node 202 receives from the publishers 402 requests topublish messages M11, M12, M13, and M14 to a channel foo. The MX node206 receives from the publishers 404 requests to publish messages M78and M79 to the channel foo. The MX node 204 receives from the publishers406 requests to publish messages M26, M27, M28, M29, M30, and M31 to thechannel foo.

The MX nodes can identify one or more streamlets for storing messagesfor the channel foo. As described earlier, each MX node can request awrite grant from the configuration manager 214 that allows the MX nodeto store the messages in a streamlet of the channel foo. For instance,the MX node 202 receives a grant from the configuration manager 214 towrite messages M11, M12, M13, and M14 to a streamlet 4101 on the Q node212. The MX node 206 receives a grant from the configuration manager 214to write messages M78 and M79 to the streamlet 4101. Here, the streamlet4101 is the last one (at the moment) of a sequence of streamlets of thechannel stream 430 storing messages of the channel foo. The streamlet4101 has messages (421) of the channel foo that were previously storedin the streamlet 4101, but is still open, i.e., the streamlet 4101 stillhas space for storing more messages and the streamlet's TTL has notexpired.

The MX node 202 can arrange the messages for the channel foo based onthe respective time that each message was received by the MX node 202,e.g., M11, M13, M14, M12 (422), and store the received messages asarranged in the streamlet 4101. That is, the MX node 202 receives M11first, followed by M13, M14, and M12. Similarly, the MX node 206 canarrange the messages for the channel foo based on their respective timethat each message was received by the MX node 206, e.g., M78, M79 (423),and store the received messages as arranged in the streamlet 4101. Otherarrangements or ordering of the messages for the channel are possible.

The MX node 202 (or MX node 206) can store the received messages usingthe method for writing data to a streamlet described earlier inreference to FIG. 3A, for example. In various implementations, the MXnode 202 (or MX node 206) can buffer (e.g., in a local data buffer) thereceived messages for the channel foo and store the received messages ina streamlet for the channel foo (e.g., streamlet 4101) when the bufferedmessages reach a predetermined number or size (e.g., 100 messages) orwhen a predetermined time (e.g., 50 milliseconds) has elapsed. Forinstance, the MX node 202 can store in the streamlet 100 messages at atime or in every 50 milliseconds. Other appropriate algorithms andtechniques, such as Nagle's algorithm, can be used for managing thebuffered messages.

In various implementations, the Q node 212 (e.g., a handler process)stores the messages of the channel foo in the streamlet 4101 in theorder as arranged by the MX node 202 and MX node 206. The Q node 212stores the messages of the channel foo in the streamlet 4101 in theorder the Q node 212 receives the messages. For instance, assume thatthe Q node 212 receives messages M78 (from the MX node 206) first,followed by messages M11 and M13 (from the MX node 202), M79 (from theMX node 206), and M14 and M12 (from the MX node 202). The Q node 212stores in the streamlet 4101 the messages in the order as received,e.g., M78, M11, M13, M79, M14, and M12, immediately after the messages421 that are already stored in the streamlet 4101. In this way, messagespublished to the channel foo from multiple publishers (e.g., 402, 404)can be serialized in a particular order and stored in the streamlet 4101of the channel foo. Different subscribers that subscribe to the channelfoo will receive messages of the channel foo in the same particularorder, as will be described in more detail in reference to FIG. 4B.

In the example of FIG. 4A, at a time instance after the message M12 wasstored in the streamlet 4101, the MX node 204 requests a grant from theconfiguration manager 214 to write to the channel foo. The configurationmanager 214 provides the MX node 204 a grant to write messages to thestreamlet 4101, as the streamlet 4101 is still open for writing. The MXnode 204 arranges the messages for the channel foo based on therespective time that each message was received by the MX node 204, e.g.,M26, M27, M31, M29, M30, M28 (424), and stores the messages as arrangedfor the channel foo.

By way of illustration, assume that the message M26 is stored to thelast available position of the streamlet 4101. As the streamlet 4101 isnow full, the Q node 212 sends to the MX node 204 a NAK message,following by an EOF message, to close the association with the MX node204 for the write grant, as described earlier in reference to FIG. 3A.The MX node 204 then requests another write grant from the configurationmanager 214 for additional messages (e.g., M27, M31, and so on) for thechannel foo.

The configuration manager 214 can monitor available Q nodes in themessaging system 100 for their respective workloads (e.g., how manystreamlets are residing in each Q node). The configuration manager 214can allocate a streamlet for the write request from the MX node 204 suchthat overloading (e.g., too many streamlets or too many read or writegrants) can be avoided for any given Q node. For instance, theconfiguration manager 214 can identify a least loaded Q node in themessaging system 100 and allocate a new streamlet on the least loaded Qnode for write requests from the MX node 204. In the example of FIG. 4A,the configuration manager 214 allocates a new streamlet 4102 on the Qnode 208 and provides a write grant to the MX node 204 to write messagesfor the channel foo to the streamlet 4102. As shown in FIG. 4A, the Qnode stores in the streamlet 4102 the messages from the MX node 204 inan order as arranged by the MX node 204: M27, M31, M29, M30, and M28(assuming that there is no other concurrent write grant for thestreamlet 4102 at the moment).

When the configuration manager 214 allocates a new streamlet (e.g.,streamlet 4102) for a request for a grant from an MX node (e.g., MX node204) to write to a channel (e.g., foo), the configuration manager 214assigns to the streamlet its TTL, which will expire after TTLs of otherstreamlets that are already in the channel's stream. For instance, theconfiguration manager 214 can assign to each streamlet of the channelfoo's channel stream a TTL of 3 minutes when allocating the streamlet.That is, each streamlet will expire 3 minutes after it is allocated(created) by the configuration manager 214. Since a new streamlet isallocated after a previous streamlet is closed (e.g., filled entirely orexpired), in this way, the channel foo's channel stream comprisesstreamlets that each expires sequentially after its previous streamletexpires. For instance, as shown in an example channel stream 430 of thechannel foo in FIG. 4A, streamlet 4098 and streamlets before 4098 haveexpired (as indicated by the dotted-lined gray-out boxes). Messagesstored in these expired streamlets are not available for reading forsubscribers of the channel foo. Streamlets 4099, 4100, 4101, and 4102are still active (not expired). The streamlets 4099, 4100, and 4101 areclosed for writing, but still are available for reading. The streamlet4102 is available for reading and writing, at the moment when themessage M28 was stored in the streamlet 4102. At a later time, thestreamlet 4099 will expire, following by the streamlets 4100, 4101, andso on.

FIG. 4B is a data flow diagram of an example method for subscribing to achannel of a messaging system. In FIG. 4B, a subscriber 480 establishesa connection 462 with an MX node 461 of the messaging system 100.Subscriber 482 establishes a connection 463 with the MX node 461.Subscriber 485 establishes a connection 467 with an MX node 468 of themessaging system 100. Here, the MX nodes 461 and 468 can respectivelycommunicate (464) with the configuration manager 214 and one or more Qnodes in the messaging system 100 via the internal network 218.

A subscriber (e.g., subscriber 480) can subscribe to the channel foo ofthe messaging system 100 by establishing a connection (e.g., 462) andsending a request for subscribing to messages of the channel foo to anMX node (e.g., MX node 461). The request (e.g., in JSON key/value pairs)can include a channel name, such as, for example, “foo.” When receivingthe subscribe request, the MX node 461 can send to the configurationmanager 214 a request for a read grant for a streamlet in the channelfoo's channel stream.

By way of illustration, assume that at the current moment the channelfoo's channel stream 431 includes active streamlets 4102, 4103, and4104, as shown in FIG. 4B. The streamlets 4102 and 4103 each are full.The streamlet 4104 stores messages of the channel foo, including thelast message (at the current moment) stored at a position 47731.Streamlets 4101 and streamlets before 4101 are invalid, as theirrespective TTLs have expired. Note that the messages M78, M11, M13, M79,M14, M12, and M26 stored in the streamlet 4101, described earlier inreference to FIG. 4A, are no longer available for subscribers of thechannel foo, since the streamlet 4101 is no longer valid, as its TTL hasexpired. As described earlier, each streamlet in the channel foo'schannel stream has a TTL of 3 minutes, thus only messages (as stored instreamlets of the channel foo) that are published to the channel foo(i.e., stored into the channel's streamlets) no earlier than 3 minutesfrom the current time can be available for subscribers of the channelfoo.

The MX node 461 can request a read grant for all available messages inthe channel foo, for example, when the subscriber 480 is a newsubscriber to the channel foo. Based on the request, the configurationmanager 214 provides the MX node 461 a read grant to the streamlet 4102(on the Q node 208) that is the earliest streamlet in the activestreamlets of the channel foo (i.e., the first in the sequence of theactive streamlets). The MX node 461 can retrieve messages in thestreamlet 4102 from the Q node 208, using the method for reading datafrom a streamlet described earlier in reference to FIG. 3B, for example.Note that the messages retrieved from the streamlet 4102 maintain thesame order as stored in the streamlet 4102. However, other arrangementsor ordering of the messages in the streamlet are possible. In variousimplementations, when providing messages stored in the streamlet 4102 tothe MX node 461, the Q node 208 can buffer (e.g., in a local databuffer) the messages and send the messages to the MX node 461 when thebuffer messages reach a predetermined number or size (e.g., 200messages) or a predetermined time (e.g., 50 milliseconds) has elapsed.For instance, the Q node 208 can send the channel foo's messages (fromthe streamlet 4102) to the MX node 461 200 messages at a time or inevery 50 milliseconds. Other appropriate algorithms and techniques, suchas Nagle's algorithm, can be used for managing the buffered messages.

After receiving the last message in the streamlet 4102, the MX node 461can send an acknowledgement to the Q node 208, and send to theconfiguration manager 214 another request (e.g., for a read grant) forthe next streamlet in the channel stream of the channel foo. Based onthe request, the configuration manager 214 provides the MX node 461 aread grant to the streamlet 4103 (on Q node 472) that logically followsthe streamlet 4102 in the sequence of active streamlets of the channelfoo. The MX node 461 can retrieve messages stored in the streamlet 4103,e.g., using the method for reading data from a streamlet describedearlier in reference to FIG. 3B, until it retrieves the last messagestored in the streamlet 4103. The MX node 461 can send to theconfiguration manager 214 yet another request for a read grant formessages in the next streamlet 4104 (on Q node 474). After receiving theread grant, the MX node 461 retrieves messages of the channel foo storedin the streamlet 4104, until the last message at the position 47731.Similarly, the MX node 468 can retrieve messages from the streamlets4102, 4103, and 4104 (as shown with dotted arrows in FIG. 4B), andprovide the messages to the subscriber 485.

The MX node 461 can send the retrieved messages of the channel foo tothe subscriber 480 (via the connection 462) while receiving the messagesfrom the Q nodes 208, 472, or 474. In various implementations, the MXnode 461 can store the retrieved messages in a local buffer. In thisway, the retrieved messages can be provided to another subscriber (e.g.,subscriber 482) when the other subscriber subscribes to the channel fooand requests the channel's messages. The MX node 461 can remove messagesstored in the local buffer that each has a time of publication that hasexceeded a predetermined time period. For instance, the MX node 461 canremove messages (stored in the local buffer) with respective times ofpublication exceeding 3 minutes. In some implementations, thepredetermined time period for keeping messages in the local buffer on MXnode 461 can be the same as or similar to the time-to-live duration of astreamlet in the channel foo's channel stream, since at a given moment,messages retrieved from the channel's stream do not include those instreamlets having respective times-to-live that had already expired.

The messages retrieved from the channel stream 431 and sent to thesubscriber 480 (by the MX node 461) are arranged in the same order asthe messages were stored in the channel stream, although otherarrangements or ordering of the messages are possible. For instance,messages published to the channel foo are serialized and stored in thestreamlet 4102 in a particular order (e.g., M27, M31, M29, M30, and soon), then stored subsequently in the streamlet 4103 and the streamlet4104. The MX node retrieves messages from the channel stream 431 andprovides the retrieved messages to the subscriber 480 in the same orderas the messages are stored in the channel stream: M27, M31, M29, M30,and so on, followed by ordered messages in the streamlet 4103, andfollowed by ordered messages in the streamlet 4104.

Instead of retrieving all available messages in the channel stream 431,the MX node 461 can request a read grant for messages stored in thechannel stream 431 starting from a message at particular position, e.g.,position 47202. For instance, the position 47202 can correspond to anearlier time instance (e.g., 10 seconds before the current time) whenthe subscriber 480 was last subscribing to the channel foo (e.g., via aconnection to the MX node 461 or another MX node of the messaging system100). The MX node 461 can send to the configuration manager 214 arequest for a read grant for messages starting at the position 47202.Based on the request, the configuration manager 214 provides the MX node461 a read grant to the streamlet 4104 (on the Q node 474) and aposition on the streamlet 4104 that corresponds to the channel streamposition 47202. The MX node 461 can retrieve messages in the streamlet4104 starting from the provided position, and send the retrievedmessages to the subscriber 480.

As described above in reference to FIGS. 4A and 4B, messages publishedto the channel foo are serialized and stored in the channel's streamletsin a particular order. The configuration manager 214 maintains theordered sequence of streamlets as they are created throughout theirrespective times-to-live. Messages retrieved from the streamlets by anMX node (e.g., MX node 461, or MX node 468) and provided to a subscribercan be, in some implementations, in the same order as the messages arestored in the ordered sequence of streamlets. In this way, messages sentto different subscribers (e.g., subscriber 480, subscriber 482, orsubscriber 485) can be in the same order (as the messages are stored inthe streamlets), regardless which MX nodes the subscribers are connectedto.

In various implementations, a streamlet stores messages in a set ofblocks of messages. Each block stores a number of messages. Forinstance, a block can store two hundred kilobytes of messages (althoughother sizes of blocks of messages are possible). Each block has its owntime-to-live, which can be shorter than the time-to-live of thestreamlet holding the block. Once a block's TTL has expired, the blockcan be discarded from the streamlet holding the block, as described inmore detail below in reference to FIG. 4C.

FIG. 4C is an example data structure for storing messages of a channelof a messaging system. As described with the channel foo in reference toFIGS. 4A and 4B, assume that at the current moment the channel foo'schannel stream 432 includes active streamlets 4104 and 4105, as shown inFIG. 4C. Streamlet 4103 and streamlets before 4103 are invalid, as theirrespective TTLs have expired. The streamlet 4104 is already full for itscapacity (e.g., as determined by a corresponding write grant) and isclosed for additional message writes. The streamlet 4104 is stillavailable for message reads. The streamlet 4105 is open and is availablefor message writes and reads.

By way of illustration, the streamlet 4104 (e.g., a computing processrunning on the Q node 474 shown in FIG. 4B) currently holds two blocksof messages. Block 494 holds messages from channel positions 47301 to47850. Block 495 holds messages from channel positions 47851 to 48000.The streamlet 4105 (e.g., a computing process running on another Q nodein the messaging system 100) currently holds two blocks of messages.Block 496 holds messages from channel positions 48001 to 48200. Block497 holds messages starting from channel position 48201, and stillaccepts additional messages of the channel foo.

When the streamlet 4104 was created (e.g., by a write grant), a firstblock (sub-buffer) 492 was created to store messages, e.g., from channelpositions 47010 to 47100. Later on, after the block 492 had reached itscapacity, another block 493 was created to store messages, e.g., fromchannel positions 47111 to 47300. Blocks 494 and 495 were subsequentlycreated to store additional messages. Afterwards, the streamlet 4104 wasclosed for additional message writes, and the streamlet 4105 was createdwith additional blocks for storing additional messages of the channelfoo.

In this example, the respective TTL's of blocks 492 and 493 had expired.The messages stored in these two blocks (from channel positions 47010 to47300) are no longer available for reading by subscribers of the channelfoo. The streamlet 4104 can discard these two expired blocks, e.g., byde-allocating the memory space for the blocks 492 and 493. The blocks494 or 495 could become expired and be discarded by the streamlet 4104,before the streamlet 4104 itself becomes invalid. Alternatively,streamlet 4104 itself could become invalid before the blocks 494 or 495become expired. In this way, a streamlet can hold one or more blocks ofmessages, or contain no block of messages, depending on respective TTLsof the streamlet and blocks, for example.

A streamlet, or a computing process running on a Q node in the messagingsystem 100, can create a block for storing messages of a channel byallocating a certain size of memory space from the Q node. The streamletcan receive, from an MX node in the messaging system 100, one message ata time and store the received message in the block. Alternatively, theMX node can assemble (i.e., buffer) a group of messages and send thegroup of messages to the Q node. The streamlet can allocate a block ofmemory space (from the Q node) and store the group of messages in theblock. The MX node can also perform compression on the group ofmessages, e.g., by removing a common header from each message orperforming other suitable compression techniques.

As described above, a streamlet (a data buffer) residing on a Q nodestores messages of a channel in the messaging system 100. To preventfailure of the Q node (a single point failure) that can cause messagesbeing lost, the messaging system 100 can replicate messages on multipleQ nodes, as described in more detail below.

FIG. 5A is a data flow diagram of an example method 500 for publishingand replicating messages of the messaging system 100. As describedearlier in reference to FIG. 4A, the MX node 204 receives messages (ofthe channel foo) from the publishers 406. The configuration manager 214can instruct the MX Node 204 (e.g., with a write grant) to store themessages in the streamlet 4102 on the Q node 208. In FIG. 5A, instead ofstoring the messages on a single node (e.g., Q node 208), theconfiguration manager 214 allocates multiple Q nodes to store multiplecopies of the streamlet 4102 on these Q nodes.

By way of illustration, the configuration manager 214 allocates Q nodes208, 502, 504, and 506 in the messaging system 100 to store copies ofthe streamlet 4102. The configuration manager 214 instructs the MX node204 to transmit the messages for the channel foo (e.g., messages M27,M31, M29, M30, and M28) to the Q node 208 (512). A computing processrunning on the Q node 208 stores the messages in the first copy (copy#1) of the streamlet 4102. Instead of sending an acknowledgement messageto the MX node 204 after storing the messages, the Q node 208 forwardsthe messages to the Q node 502 (514). A computing process running on theQ node 502 stores the messages in another copy (copy #2) of thestreamlet 4102. Meanwhile, the Q node 502 forwards the messages to the Qnode 504 (516). A computing process running on the Q node 504 stores themessages in yet another copy (copy #3) of the streamlet 4102. The Q node504 also forwards the message to the Q node 506 (518). A computingprocess running on the Q node 506 stores the messages in yet anothercopy (copy #4) of the streamlet 4102. The Q node 506 can send anacknowledgement message to the MX node 204, indicating that all themessages (M27, M31, M29, M30, and M28) have been stored successfully instreamlet copies #1, #2, #3 and #4.

In some implementations, after successfully storing the last copy (copy#4), the Q node 506 can send an acknowledgement to its upstream Q node(504), which in turns sends an acknowledgement to its upstream Q node(502), and so on, until the acknowledgement is sent to the Q node 208storing the first copy (copy #1). The Q node 208 can send anacknowledgement message to the MX node 204, indicating that all messageshave been stored successfully in the streamlet 4102 (i.e., in the copies#1, #2, #3 and #4).

In this way, four copies of the streamlet 4102 (and each message in thestreamlet) are stored in four different Q nodes. Other numbers (e.g.,two, three, five, or other suitable number) of copies of a streamlet arealso possible. In the present illustration, the four copies form a chainof copies including a head copy in the copy #1 and a tail copy in thecopy #4. When a new message is published to the streamlet 4102, themessage is first stored in the head copy (copy #1) on the Q node 208.The message is then forwarded downstream to the next adjacent copy, thecopy #2 on the Q node 502 for storage, then to the copy #3 on the Q node504 for storage, until the message is stored in the tail copy the copy#4 on the Q node 506.

In addition to storing and forwarding by messages, the computingprocesses running on Q nodes that store copies of a streamlet can alsostore and forward messages by blocks of messages, as described earlierin reference to FIG. 4C. For instance, the computing process storing thecopy #1 of the streamlet 4102 on Q node 208 can allocate memory andstore a block of, for example, 200 kilobytes of messages (although othersizes of blocks of messages are possible), and forward the block ofmessages to the next adjacent copy (copy #2) of the chain for storage,and so on, until the block messages is stored in the tail copy (copy #4)on the Q node 506.

Messages of the streamlet 4102 can be retrieved and delivered to asubscriber of the channel foo from one of the copies of the streamlet4102. FIG. 5B is a data flow diagram of an example method 550 forretrieving stored messages in the messaging system 100. For instance,the subscriber 480 can send a request for subscribing to messages of thechannel to the MX node 461, as described earlier in reference to FIG.4B. The configuration manager 214 can provide to the MX node 461 a readgrant for one of the copies of the streamlet 4102. The MX node 461 canretrieve messages of the streamlet 4102 from one of the Q nodes storinga copy of the streamlet 4102, and provide the retrieved messages to thesubscriber 480. For instance, the MX node 461 can retrieve messages fromthe copy #4 (the tail copy) stored on the Q node 506 (522). As foranother example, the MX node 461 can retrieve messages from the copy #2stored on the Q node 502 (524). In this way, the multiple copies of astreamlet (e.g., copies #1, #2, #3, and #4 of the streamlet 4102)provide replication and redundancy against failure if only one copy ofthe streamlet were stored in the messaging system 100. In variousimplementations, the configuration manager 214 can balance workloadsamong the Q nodes storing copies of the streamlet 4102 by directing theMX node 461 (e.g., with a read grant) to a particular Q node that has,for example, less current read and write grants as compared to other Qnodes storing copies of the streamlet 4102.

A Q node storing a particular copy in a chain of copies of a streamletmay fail, e.g., a computing process on the Q node storing the particularcopy may freeze. Other failure modes of a Q node are possible. An MXnode can detect a failed node (e.g., from non-responsiveness of thefailed node) and report the failed node to a configuration manager inthe messaging system 100 (e.g., configuration manager 214). A peer Qnode can also detect a failed Q node and report the failed node to theconfiguration manager. For instance, an upstream Q node may detect afailed downstream Q node when the downstream Q node is non-responsive,e.g., fails to acknowledge a message storage request from the upstream Qnode as described earlier. It is noted that failure of a Q node storinga copy of a particular streamlet of a particular channel stream does nothave to be for publish or subscribe operations of the particularstreamlet or of the particular channel stream. Failure stemming fromoperations on another streamlet or another channel stream can also alerta configuration manager about failure of a Q node in the messagingsystem 100.

When a Q node storing a particular copy in a chain of copies of astreamlet fails, a configuration manager in the messaging system 100 canrepair the chain by removing the failed node, or by inserting a new nodefor a new copy into the chain, for example. FIGS. 5C and 5D are dataflow diagrams of example methods for repairing a chain of copies of astreamlet in the messaging system 100. In FIG. 5C, for instance, afterdetecting that the Q node 504 fails, the configuration manager 214 canrepair the chain of copies by redirecting messages intended to be storedin the copy #3 of the streamlet 4102 on the Q node 502 to the copy #4 ofthe streamlet 4102 on the Q node 506. In this example, a message (or ablock of messages) is first sent from the MX node 204 to the Q node 208for storage in the copy #1 of the streamlet 4102 (572). The message thenis forwarded to the Q node 502 for storage in the copy #2 of thestreamlet 4102 (574). The message is then forwarded to the Q node 506for storage in the copy #4 of the streamlet 4102 (576). The Q node 506can send an acknowledgement message to the configuration manager 214indicating that the message has been stored successfully.

Here, a failed node can also be the node storing the head copy or thetail copy of the chain of copies. For instance, if the Q node 208 fails,the configuration manager 214 can instruct the MX node 204 first to sendthe message to the Q node 502 for storage in the copy #2 of thestreamlet 4102. The message is then forwarded to the next adjacent copyin the chain for storage, until the message is stored in the tail copy.

If the Q node 506 fails, the configuration manager 214 can repair thechain of copies of the streamlet 4102 such that the copy #3 on the Qnode 504 becomes the tail copy of the chain. A message is first storedin the copy #1 on the Q node 208, then subsequently stored in the copy#2 on the Q node 502, and the copy #3 on the Q node 504. The Q node 504then can send an acknowledgement message to the configuration manager214 indicating that the message has been stored successfully.

In FIG. 5D, the configuration manager 214 replaces the failed node Qnode 504 by allocating a new Q node 508 to store a copy #5 of the chainof copies of the streamlet 4102. In this example, the configurationmanager 214 instructs the MX node 204 to send a message (from thepublishers 406) to the Q node 208 for storage in the copy #1 of thestreamlet 4102 (582). The message is then forwarded to the Q node 502for storage in the copy #2 of the streamlet 4102 (584). The message isthen forwarded to the Q node 508 for storage in the copy #5 of thestreamlet 4012 (586). The message is then forwarded to the Q node 506for storage in the copy #4 of the streamlet 4102 (588). The Q node 506can send an acknowledgement message to the configuration manager 214indicating that the message has been stored successfully.

FIG. 6 is a data flow diagram 600 illustrating the application ofselective filtering, searching, transforming, querying, aggregating andtransforming of messages in real time to manage the delivery of messagesinto and through each channel and on to individual subscribers. Usersoperating applications on client devices, such as, for example,smartphones, tablets, and other internet-connected devices, act assubscribers (e.g., subscriber 480 in FIG. 4B, subscriber 602 in FIG. 6).The applications may be, for example, consumers of the messages toprovide real-time information about news, transportation, sports,weather, or other subjects that rely on published messages attributed toone or more subjects and/or channels. Message publishers 604 can be anyinternet-connected service that provides, for example, status data,transactional data or other information that is made available to thesubscribers 602 on a subscription basis. In some versions, therelationship between publishers and channels is 1:1, that is there isone and only one publisher that provides messages into that particularchannel. In other instances, the relationship may be many-to-one (morethan one publisher provides messages into a channel), one-to-many (apublisher's messages are sent to more than one channel), or many-to-many(more than one publisher provides messages to more than one channel).Typically, when a subscriber subscribes to a channel, they receive allmessages and all message data published to the channel as soon as it ispublished. The result, however, is that many subscribers can receivemore data (or data that requires further processing) than is useful. Theadditional filtering or application of functions against the data placesundue processing requirements on the subscriber application and candelay presentation of the data in its preferred format.

A filter 606 can be created by providing suitable query instructions at,for example, the time the subscriber 602 subscribes to the channel 608.The filter 606 that is specified can be applied to all messagespublished to the channel 608 (e.g., one message at a time), and can beevaluated before the subscriber 602 receives the messages (e.g., seeStep 2 in FIG. 6). By allowing subscribers 602 to create queryinstructions a priori, that is upon subscribing to the channel 608 andbefore data is received into the channel 608, the burden of filteringand processing messages moves closer to the data source, and can bemanaged at the channel level. As a result, the messages are pre-filteredand/or pre-processed before they are forwarded to the subscriber 602.Again, the query instructions need not be based on any a prioriknowledge of the form or substance of the incoming messages. The queryinstructions can be used to pre-process data for applications such as,for example, real-time monitoring services (for transportation,healthcare, news, sports, weather, etc.) and dashboards (e.g.,industrial monitoring applications, financial markets, etc.) to filterdata, summarize data and/or detect anomalies. One or more filters 606can be applied to each channel 608.

The query instructions can implement real-time searches and queries,aggregate or summarize data, or transform data for use by a subscriberapplication. In some embodiments, including those implementing JSONformatted messages, the messages can be generated, parsed andinterpreted using the query instructions, and the lack of a pre-definedschema (unlike conventional RDBMS/SQL-based applications) means that thequery instructions can adapt to changing business needs without the needfor schema or application layer changes. This allows the queryinstructions to be applied selectively at the message level within achannel, thus filtering and/or aggregating messages within the channel.In some instances, the queries may be applied at the publisherlevel—meaning channels that receive messages from more than onepublisher may apply certain filters against messages from specificpublishers. The query instructions may be applied on a going-forwardbasis, that is on only newly arriving messages, and/or in some cases,the query instructions may be applied to historical messages alreadyresiding in the channel queue.

The query instructions can be applied at either or both of the ingressand egress side of the PubSub service. On the egress side, the queryinstructions act as a per-connection filter against the messagechannels, and allows each subscriber to manage their own set of uniquefilters. On the ingress side, the query instructions operate as acentralized, system-wide filter that is applied to all publishedmessages.

For purposes of illustration and not limitation, examples of queryinstructions that may be applied during message ingress include:

-   -   A message may be distributed to multiple channels or to a        different channel (e.g., based on geo-location in the message,        or based on a hash function of some value in the message).    -   A message may be dropped due to spam filtering or DoS rules        (e.g., limiting the number of messages a publisher can send in a        given time period).    -   An alert message may be sent to an admin channel on some event        arriving at any channel (e.g., cpu_temp>threshold).

For purposes of illustration and not limitation, examples of queryinstructions that may be applied during message egress include:

-   -   Channels that contain events from various sensors where the user        is only interested in a subset of the data sources.    -   Simple aggregations, where a system reports real time events,        such as cpu usage, sensor temperatures, etc., and we would like        to receive some form of aggregation over a short time period,        irrespective of the number of devices reporting or the reporting        frequency, e.g., average(cpu_load), max(temperature),        count(number_of_users), count(number of messages) group by        country.    -   Transforms, where a system reports real time events and metadata        is added to them from mostly static external tables, e.g.,        adding a city name based on IP address, converting an        advertisement ID to a marketing campaign ID or to a marketing        partner ID.    -   Adding default values to event streams where such values do not        exist on certain devices.    -   Advanced aggregations, where a system reports real time events,        and combines some mostly static external tables data into the        aggregation in real time, e.g., grouping advertisement clicks by        partners and counting number of events.    -   Counting number of user events, grouping by a/b test cell        allocation.

In some embodiments, the query instructions may be used to define anindex or other suitable temporary data structure, which may then beapplied against the messages as they are received into the channel toallow for the reuse of the data element(s) as searchable elements. Insuch cases, a query frequency may be maintained to describe the numberof times (general, or in a given period) that a particular data elementis referred to or how that element is used. If the frequency that thedata element is used in a query exceeds some threshold, the index may bestored for subsequent use on incoming messages, whereas in otherinstances in which the index is used only once (or infrequently) it maybe discarded. In some instances, the query instruction may be applied tomessages having arrived at the channel prior to the creation of theindex. Thus, the messages are not indexed according to the data elementsdescribed in the query instructions but processed using the queryinstructions regardless, whereas messages arriving after the creation ofthe index may be filtered and processed using the index. For queries orother subscriptions that span the time at which the index may have beencreated, the results of applying the query instructions to the messagesas they are received and processed with the index may be combined withresults of applying the query instructions to non-indexed messagesreceived prior to receipt of the query instructions.

For purposes of illustration and not limitation, one use case for such afiltering application is a mapping application that subscribes to publictransportation data feeds, such as the locations of all buses across acity. The published messages may include, for example, geographic datadescribing the location, status, bus agency, ID number, route number,and route name of the buses. Absent pre-defined query instructions, theclient application would receive individual messages for all buses.However, query instructions may be provided that filter out, forexample, inactive routes and buses and aggregate, for example, a countof buses by agency. The subscriber application receives the filtered busdata in real time and can create reports, charts and other user-definedpresentations of the data. When new data is published to the channel,the reports can be updated in real time based on a period parameter(described in more detail below).

The query instructions can be provided (e.g., at the time the subscribersubscribes to the channel) in any suitable format or syntax. Forexample, the following illustrates the structure of several fields of asample subscription request Protocol Data Unit (PDU) with the PDU keysspecific to adding a filter to a subscription request:

{ ″action″: ″subscribe″, “body”: { ″channel″: “ChannelName” ″filter″:“QueryInstructions” ″period″: [1-60, OPTIONAL] } }In the above subscription request PDU, the “channel” field can be avalue (e.g., string or other appropriate value or designation) for thename of the channel to which the subscriber wants to subscribe. The“filter” field can provide the query instructions or other suitablefilter commands, statements, or syntax that define the type ofkey/values in the channel message to return to the subscriber. The“period” parameter specifies the time period in, for example, seconds,to retain messages before returning them to the subscriber (e.g., aninteger value from 1 to 60, with a default of, for example, 1). The“period” parameter will be discussed in more detail below. It is notedthat a subscription request PDU can include any other suitable fields,parameters, or values.

One example of a query instruction is a “select” filter, which selectsthe most recent (or “top”) value for all (e.g., “select.*”) or selected(e.g., “select.name”) data elements. In the example below, the Filtercolumn shows the filter value sent in the query instructions as part ofa subscription as the filter field. The Message Data column lists theinput of the channel message data and the message data sent to theclient as output. In this example, the value for the “extra” key doesnot appear in the output, as the “select” filter can return only thefirst level of results and does not return any nested key values.

Filter Message Data SELECT * Input {“name”: “art”, “eye”: “blue”),{“name”: “art”, “age”: 11}, {“age”: 12, “height”: 190} Output {“name”:“art”, “age”: 12, “eye”: “blue”, “height”: 190} SELECT Input top.*{“top”: {“age”: 12, “eyes”: “blue”}}, {“top”: {“name”: “joy”, “height”:168), “extra”: 1}, {“top”: {“name”: “art”}} Output {“name”: “art”,“age”: 12, “eye”: “blue”, “height”: 168}

For aggregative functions, all messages can be combined that satisfy thequery instructions included in the GROUP BY clause. The aggregatedvalues can then be published as a single message to the subscriber(s) atthe end of the aggregation period. The number of messages that areaggregated depends on, for example, the number of messages received inthe channel in the period value for the filter. For instance, if theperiod parameter is set to 1, and 100 messages are received in onesecond, all 100 messages are aggregated into a single message fortransmission to the subscsriber(s). As an example, a query instructionas shown below includes a filter to aggregate position data for anobject, grouping it by obj_id, with a period of 1:

SELECT*WHERE(<expression with aggregate function>)GROUP BY obj_id

In this example, all messages published in the previous second with thesame obj_id are grouped and sent as a batch to the subscriber(s).

In some embodiments, a MERGE(*) function can be used to change howaggregated message data is merged. The MERGE(*) function can return arecursive union of incoming messages over a period of time. The mergefunction may be used, for example, to track location data for an object,and the subscriber is interested in the most recent values for allkey/value pairs contained in a set of aggregated messages. The followingstatement shows an exemplary syntax for the MERGE(*) function:

SELECT[expr][name,]MERGE(*)[·*][AS name][FROM expr][WHERE expr][HAVINGexpr]GROUP BY name

The following examples illustrate how the MERGE(*) function may beapplied within query instructions to various types of channel messages.In the following examples, the Filter column shows the filter valueincluded in the query instructions as part of a subscription request asthe FILTER field. The Message Data column lists the Input channelmessage data and the resulting message data sent to the subscriber asOutput. The filter returns the most recent values of the keys identifiedin the input messages, with the string MERGE identified as the columnname in the output message data. The first example below shows theMERGE(*) function in a filter with a wildcard, for the message data isreturned using the keys from the input as column names in the output.

Filter Message Data SELECT Input MERGE(*) {“name”: “art”, “age”: 10},{“name”: “art”, “age”: 11, “items”: [0]} Output {“MERGE”: {“name”:“art”, “age”: 11, “items”: [0]}}The next example illustrates the use of the MERGE(*) function in afilter using a wildcard and the “AS” statement with a value of MERGE.The output data includes MERGE as the column name.

Filter Message Data SELECT Input MERGE(*).* { “name”: “art”, “age”: 12,“items”: [0], “skills”: { “work”: [“robots”] } }, { “name”: “art”,“age”: 13, “items”: [“car”], “skills”: { “home”: [“cooking”] } } Output{ “name”: “art”, “age”: 13, “items”: [“car”], “skills”: { “work”:[“robots”], “home”: [“cooking”] } } SELECT Input MERGE(top.*) {“top”: {}, “garbage”: 0}, AS merge {“top”: {“name”: “art”, “eyes”: “blue”}},{“top”: {“name”: “joy”, “height”: 170}} Output {“merge”: {“name”: “joy”,“eyes”: “blue”, “height”: 170}}

Generally, for aggregative functions and for filters that only include aSELECT(expr) statement, only the latest value for any JSON key in themessage data from the last message received can be stored and returned.Therefore, if the most recent message received that satisfies the filterstatement is missing a key value identified in a previously processedmessage, that value is not included in the aggregate, which could resultin data loss. However, filters that also include the MERGE(*) functioncan retain the most recent value for all keys that appear in messages toan unlimited JSON object depth. Accordingly, the most recent version ofall key values can be retained in the aggregate.

The MERGE(*) function can be used to ensure that associated values forall keys that appear in any message during the aggregation period alsoappear in the final aggregated message. For example, a channel may trackthe physical location of an object in three dimensions: x, y, and z.During an aggregation period of one second, two messages are publishedto the channel, one having only two parameters: OBJ{x:1, y:2, z:3} andOBJ{x:2, y:3}. In the second message, the z value did not change and wasnot included in the second message. Without the MERGE(*) function, theoutput result would be OBJ{x:2, y:3}. Because the z value was notpresent in the last message in the aggregation period, the z value wasnot included in the final aggregate. However, with the MERGE(*)function, the result is OBJ{x:2, y:3, z:3}.

The following table shows one set of rules that may be used to aggregatedata in messages, depending on the type of data. For arrays, elementsneed not be merged, but instead JSON values can be overwritten for thearray in the aggregate with the last array value received.

Type of Data to Aggregate Without With JSON Data {msg1}, {msg2} MERGE(*)MERGE(*) Additional {a: 1, b: 2}, {c: 3} {c: 3} {a: 1, b: 2, c: 3}key/value Dififerent {a: 2}, {a: “2”} {a: “2”} {a: “2”} value datatypeMissing {a: 2}, { } {a: 2} {a: 2} key/value null value {a: 2}, {a: null}{a: null} {a: null} Dififerent {a: {b: 1}}, {a: {c: 2}} {a: {c: 2}} {a:{b: 1, c: 2}} key value Arrays {a: [1, 2]}, {a: [3, 4]} {a: [3, 4]} {a:[3, 4]}

The query instructions can be comprised of one or more suitable filtercommands, statements, functions, or syntax. For purposes of illustrationand not limitation, in addition to the SELECT and MERGE functions, thequery instructions can include filter statements or functions, such as,for example, ABS(expr), AVG(expr), COALESCE(a[, b . . . ]), CONCAT(a[, b. . . ]), COUNT(expr), COUNT_DISTINCT(expr), IFNULL(expr1, expr2),JSON(expr), MIN(expr[, expr1, . . . ]), MAX(expr[, expr1, . . . ]),SUBSTR(expr, expr1[, expr2]), SUM(expr), MD5(expr), SHA1(expr),FIRST_VALUE(expr) OVER (ORDER BY expr1), and/or LAST_VALUE(expr) OVER(ORDER BY expr1), where “expr” can be any suitable expression that iscapable of being processed by a filter statement or function, such as,for example, a SQL or SQL-like expression. Other suitable filtercommands, statements, functions, or syntax are possible for the queryinstructions.

According to the present invention, non-filtered queries can translateto an immediate copy of the message to the subscriber, without any JSONor other like processing. Queries that include a SELECT filter command(without aggregation) can translate into an immediate filter. Ininstances in which the messages are formatted using JSON, each messagemay be individually parsed and any WHERE clause may be executed directlyon the individual message as it arrives, without the need for creatingindices or other temporary data structures. If the messages pass theWHERE clause filter, the SELECT clause results in a filtered messagethat can be converted back to its original format or structure (e.g.,JSON) and sent to the subscriber.

Aggregative functions, such as, for example, COUNT( ), SUM( ), AVG( ),and the like, can translate into an immediate aggregator. In instancesin which the messages are formatted using JSON, each message may beindividually parsed and any WHERE clause may be executed directly on theindividual message as it arrives, without the need for creating indicesor other temporary data structures. If a WHERE clause is evaluated,messages passing such criteria are aggregated (e.g., aggregates in theSELECT clause are executed, thereby accumulating COUNT, SUM, AVG, and soforth) using the previous accumulated value and the value from theindividual message. Once per aggregation period (e.g., every 1 second),the aggregates are computed (e.g., AVG=SUM/COUNT), and the SELECT clauseoutputs the aggregated message, which can be converted to its originalformat or structure (e.g., JSON) and sent to the subscriber.

More complex aggregative functions, such as, for example, GROUP BY,JOIN, HAVING, and the like, can be translated into a hash tableaggregator. Unlike SELECT or other like functions that can use aconstant memory, linearly expanding memory requirements can be dependentupon the results of the GROUP BY clause. At most, grouping by a uniquevalue (e.g., SSN, etc.) can result in a group for each individualmessage, but in most cases grouping by a common data element (e.g.,user_id or other repeating value) can result in far fewer groups. Inpractice, each message is parsed (from its JSON format, for example).The WHERE clause can be executed directly on the individual message asit arrives, without creating indices or other temporary structures. Ifthe WHERE clause is satisfied, the GROUP BY expressions can be computeddirectly and used to build a hash key for the group. The aggregativefunctions in the SELECT clause can be executed, accumulating COUNT, SUM,AVG, or other functions using the previous accumulated value specificfor the hash key (group) and the value from the individual message. Onceper aggregation period (e.g., every 1 second), the aggregates arecomputed (e.g., AVG=SUM/COUNT) for each hash key (group), and the SELECTclause can output the aggregated message for each hash key to beconverted back to its original format or structure (e.g., JSON) and sentto the subscriber (e.g., one message per hash key (group)).

In embodiments in which the aggregation period is limited (e.g., 1second-60 seconds) and the network card or other hardware/throughputspeeds may be limited (e.g., 10/gbps), the overall maximal memoryconsumption can be calculated as time*speed (e.g., 1 GB per second, or60 GB per minute). Hence, the upper bound is independent of the numberof subscribers. In certain implementations, each message only need beparsed once (e.g., if multiple filters are set by multiple clients) andonly if needed based on the query instructions, as an empty filter doesnot require parsing the message.

Referring to FIG. 7A, subscriptions can include a “period” parameter,generally defined in, for example, seconds and in some embodiments canrange from 1 to 60 seconds, although other time increments and timeranges are possible. The period parameter(s) can be purely sequential(e.g., ordinal) and/or time-based (e.g., temporal) and included in theself-described data and therefore available for querying, aggregation,and the like. For example, FIG. 7A illustrates the filter processaccording to the present invention for the first three seconds with aperiod of 1 second. In the present example, the subscription starts att=0. The filter created from the query instructions is applied againstall messages received during each 1-second period (e.g., one message ata time). The results for each period are then batched and forwarded tothe subscriber. Depending on the query instructions used, the messagescan be aggregated using the aggregation functions discussed previouslybefore the message data is sent to the subscriber.

In some cases, the process defaults to sending only new, incomingmessages that meet the query instructions on to the subscriber. However,a subscriber can subscribe with history and use a filter, such that thefirst message or messages sent to the subscriber can be the historicalmessages with the filter applied. Using the period of max_age and/or a“next” parameter provides additional functionality that allows forretrieval and filtering of historical messages.

More particularly, a max_age parameter included with the queryinstructions can facilitate the retrieval of historical messages thatmeet this parameter. FIG. 7B illustrates an example of a max_ageparameter of 2 seconds (with a period of 1 second) that is provided withthe query instructions. The filter created from the query instructionsis applied to the historical messages from the channel that arrived fromt−2 through t=0 (t=0 being the time the subscription starts), and to themessages that arrived in the first period (from t=0 to t+1). Thesemessages can be sent in a single batch to the subscriber (as Group 1).The filter is applied to each message in each subsequent period (e.g.,from t+1 to t+2 as Group 2) to batch all messages that meet the queryinstructions within that period. Each batch is then forwarded on to thesubscriber.

When a subscriber subscribes with a “next” parameter to a channel with afilter, the filter can be applied to all messages from the next value upto the current message stream position for the channel, and the resultscan be sent to the subscriber in, for example, a single batch. Forexample, as illustrated in FIG. 7C, a next parameter is included withthe query instructions (with a period of 1 second). The next parameterinstructs the process to apply the filter created from the queryinstructions to each message from the “next position” up through thecurrent stream position (e.g., up to t=0) and to the messages thatarrived in the first period (from t=0 to t+1). These messages can besent in a single batch to the subscriber (as Group 1). The filter isapplied to each message in each subsequent period (e.g., from t+1 to t+2as Group 2) to batch all messages that meet the query instructionswithin that period. Each batch is then forwarded on the subscriber.

When a subscriber subscribes with a next parameter, chooses to receivehistorical messages on a channel, and includes a filter in thesubscription, the subscriber can be updated to the current messagestream position in multiple batches. FIG. 7D illustrates an example of amax_age parameter of 2 seconds (with a period of 1 second) and a nextparameter that can be combined into one set of query instructions. Thefilter created from the query instructions is applied to the historicalmessages from the channel that arrived from the end of the history tothe “next” value of the subscription (i.e., from 2 seconds before thenext value up to the next value), to the messages from the next value tothe current stream position (e.g., up to t=0), and to the messages thatarrived in the first period (from t=0 to t+1). These messages can besent in a single batch to the subscriber (as Group 1). The filter isapplied to each message in each subsequent period (e.g., from t+1 to t+2as Group 2) to batch all messages that meet the query instructionswithin that period. Each batch is then forwarded on the subscriber.Consequently, historical messages can be combined with messages thatstart at a particular period indicator and batched for transmission tothe subscriber.

The query instructions can define how one or more filters can be appliedto the incoming messages in any suitable manner. For example, theresulting filter(s) can be applied to any or all messages arriving ineach period, to any or all messages arriving across multiple periods, toany or all messages arriving in select periods, or to any or allmessages arriving on a continuous or substantially continuous basis(i.e., without the use of a period parameter such that messages are notretained before returning them to the subscriber). Such filteredmessages can be batched in any suitable manner or sent individually(e.g., one message at a time) to subscribers. In particular, thefiltered messages can be sent to the subscriber in any suitable formator syntax. For example, the following illustrates the structure ofseveral fields of a sample channel PDU that contains the message resultsfrom a filter request:

{ ″action″: ″channel/data″, “body”: { ″channel″: ChannelName ″next″:ChannelStreamPosition ″messages″: [ChannelData]+ // Can be one or moremessages } }In the above channel PDU, the “channel” field can be a value (e.g.,string or other appropriate value or designation) of the channel name towhich the subscriber has subscribed. The “next” field can provide thechannel stream position of the batch of messages returned in the channelPDU. The “messages” field provides the channel data of the messagesresulting from application of the specified filter. One or more messagescan be returned in the “messages” field in such a channel PDU. It isnoted that a channel PDU can include any other suitable fields,parameters, values, or data.

Turning now to FIG. 8, an example diagram of a map 800 (e.g., radio map)is illustrated. As depicted, the map 800 may include a map of anenvironment 801. The environment 801 may include, for example, an indoorenvironment (e.g., inside of residential, commercial, or industrialbuildings), an outdoor environment (e.g., a public or private campus, aneighborhood, a town, a city, a country, a geographical region, and soforth), or a combination of an indoor and outdoor environments (e.g., abuilding and adjacent parking and recreational areas). As illustrated,the map 800 may include a number of locations 802 (e.g., rooms within abuilding, or specific areas or regions of a campus) by which a numbernodes 804, 806, 808, 810, 812, 814, 816, 818, 820, 822, and 824 mayeither be located or move within and about. In certain embodiments, eachof the nodes 804, 806, 808, 810, 812, 814, 816, 818, 820, 822, and 824may represent any of various client electronic devices (e.g., mobileelectronic devices [e.g., mobile phones, tablet computers, laptopcomputers, cameras], in-home electronic devices [e.g., video gameconsoles, smoke detectors, thermostats, gateway devices, desktopcomputers, projectors], wearable electronic devices [e.g., smartwatches,wristbands, pedometers, electronic eyewear, electronic headwear], and soforth) that may be associated with users stationary or moving within orabout the environment 801.

In one embodiment, each of the nodes 804, 806, 808, 810, 812, 814, 816,818, 820, 822, and 824 may be connected to a wireless local area network(WLAN) (e.g., Wi-Fi™, UWB, White-Fi, and so forth), a cellular network(e.g., 4G, LTE™, 5G, LTE-LAA™, LTE-U™, and so forth), personal areanetwork (PAN) (e.g., 6LowWPAN™, RuBee™, Z-Wave, ZigBee™, WANET, and soforth), or other similar wireless network within or about theenvironment 801. As will be further appreciated with respect to FIG. 9,the nodes 804, 806, 808, 810, 812, 814, 816, 818, 820, 822, and 824 mayeach be in communication with one or more server electronic devices tosend and receive messages regarding an approximate geolocation of thenodes 804, 806, 808, 810, 812, 814, 816, 818, 820, 822, and 824 withrespect to the environment 801.

In certain embodiments, as further illustrated by FIG. 8, some of thenodes 808, 812, 814, 816, 818, and 820 may include adjacent arrowillustrations while others may not. Specifically, in accordance with thepresent embodiments, the nodes 808, 812, 814, 816, 818, and 820 (e.g.,those including no adjacent arrow illustrations) may be stationarywithin or between pathways of the environment 801. Similarly, nodes 804,806, 810, 822, and 824 (e.g., those including the adjacent arrowillustrations) may be moving within or between pathways of theenvironment 801. It should be appreciated that each of the nodes 804,806, 808, 810, 812, 814, 816, 818, 820, 822, and 824 may, at differenttimes, for example, be stationary or mobile within or about theenvironment 801. In accordance with the present embodiments, a serverelectronic device in communication with the nodes 804, 806, 808, 810,812, 814, 816, 818, 820, 822, and 824 may determine and track thegeolocation of the nodes 804, 806, 808, 810, 812, 814, 816, 818, 820,822, and 824 within or about the environment 801.

For example, as will be further appreciated with respect to FIGS. 9-12,the present embodiments may include a system architecture for locatingand tracking electronic devices in indoor and outdoor environments. Themessaging system may support the PubSub communication pattern and mayallow publishers and subscribers to publish and receive live messages.Users of certain client electronic devices may want to determine andtrack other electronic devices, and thus may be both publishers andsubscribers of the messaging system. Electronic devices may publishmessages to indicate the approximate geolocations of the electronicdevices. Users may view the messages corresponding to live approximategeolocations of the electronic devices as the users of the electronicdevices move within or about indoor or outdoor environments. Forexample, as will be described below, the present embodiments may bedirected to a schema for collaborative geo-positioning of multipleelectronic devices that may improve triangulation accuracy and precisionof locating each electronic device of a number of electronic devices bycalculating and recording information that may be used to improveaccuracy and precision of future approximate geolocations. The presenttechniques also minimize the amount of information stored on serversthat could be used to track and locate devices in indoor and outdoorenvironments or even worldwide in some examples. In this way, thepresent embodiments may provide techniques to efficiently determine andtrack the approximate geolocation of electronic devices within or aboutindoor and outdoor environments, or otherwise in any of variousenvironments in which large-scale satellite systems may be inaccurate,imprecise, or otherwise unavailable.

With the foregoing in mind, FIG. 9 is a diagram of an example systemarchitecture 900 that may be used to track and locate client electronicdevices stationary or moving within or about, for example, theenvironment 801. In accordance with at least some of the presentembodiments, the system architecture 900 may include a geo-positioningnetwork (GPSNET), and, in some embodiments, may correspond to the system100 discussed above with respect to FIGS. 1A and 1B. For example, asdepicted, the system architecture 900 may include a server electronicdevice 901 (or numerous server electronic devices 901) and a number ofclient electronic devices 916, 918, 920, 922, and 924 (e.g.,corresponding to one or more of the nodes 804, 806, 808, 810, 812, 814,816, 818, 820, 822, and 824 discussed in FIG. 8) that may be incommunication with the server electronic device 901. The serverelectronic device 901 may support the PubSub communication pattern, asdescribed earlier in reference to FIGS. 1A through 5D. In someembodiments, the server electronic device 901 may be referred to as aPubSub system or a PubSub messaging system. As illustrated, the serverelectronic device 901 may include, for example, a channel 902, a channel904, a channel 906, a channel 908, a channel 910, a channel 912, and achannel 914. The messages published to channels 902, 904, 906, 908, 910,912, and 914 (e.g., channel streams) may be divided into streamlets,which may be stored within Q nodes or one more databases of the serverelectronic device 901, as generally described, for example, earlier inreference to FIGS. 1A through 5D. C nodes of the messaging system may beused to offload data transfers from one or more Q nodes (e.g., to cachesome of the streamlets stored in the Q nodes).

In certain embodiments, the client electronic devices 916, 918, 920,922, and 924 may establish respective persistent connections (e.g., TCPcommunications or other similar communications channels) to one or moreMX nodes. The one or more MX nodes may serve as termination points forthese connections, as described earlier in reference to FIGS. 1A through5D. As further illustrated, each of the client electronic devices 916,918, 920, 922, and 924 may include one or more respective applicationcomponents 926, 928, 930, 932, and 934, which may, for example, allowusers to subscribe to and publish to the channels 902, 904, 906, 908,910, 912, and 914 of the server electronic device 901. For example, theserver electronic device 901 may authenticate users and determinewhether users are allowed to publish to certain channels 902, 904, 906,908, 910, 912, and 914, in some embodiments.

Turning now to FIG. 10, which illustrates a flow diagram of a method1000 of determining and tracking the approximate geolocation of one ormore client electronic devices (e.g., client electronic devices 916,918, 920, 922, and 924 discussed with respect to FIG. 9) in accordancewith the present embodiments. In certain embodiments, the method 1000may be performed by processing logic that may include hardware such asone or more computer processing devices, software (e.g., instructionsrunning/executing on a computer processing device), firmware (e.g.,microcode), or a combination thereof, such as the server electronicdevice 901 discussed above with respect to FIG. 9. For the purpose ofillustration and breadth, henceforth, the method 1000 of FIG. 10, themethod 1100 of FIG. 11, and the method 1200 of FIG. 12 will be describedin conjunction with various examples and in reference to FIGS. 8 and 9to illuminate and delineate the present techniques.

The method 1000 of FIG. 10 may begin with a computer processing deviceof the server electronic device 901 receiving desired operationalparameters from one or more of a plurality of client electronic devices(Step 1002). For example, referring again to FIG. 9, one or more of theclient electronic devices 916, 918, 920, 922, and 924 may transmit arequest to the server electronic device 901 in which the request mayinclude a message to the server electronic device 901 updating, forexample, user preferences. In some embodiments, the user preferences maybe updated by the application components 926, 928, 930, 932, and 934running on the client electronic devices 916, 918, 920, 922, and 924,and may include, for example, desired operational parameters such asfrequency, accuracy, power consumption, processing capacity, bandwidth,hardware usage, and similar operational parameters that may be specifiedby a user of the client electronic devices 916, 918, 920, 922, and 924.

For example, frequency may include a measurement of time specifying theinterval at which the server electronic device 901 may endeavor toproduce a location estimate. Similarly, accuracy may include ameasurement of distance d and a measurement of probability p, specifyingthat the server electronic device 901 may endeavor to return deliveryconfidences such that there is a p-percent or greater probability thatthe client electronic devices 916, 918, 920, 922, and 924 true locationis within d distance of the delivered estimate. Power consumption, forexample, may include two unit-less values indicating, for example, auser's willingness to sacrifice desired frequency and accuracy inexchange for lower power consumption (e.g., battery usage). Processingcapacity may include two unit-less numbers indicating, for example, auser's willingness to sacrifice desired frequency and accuracy inexchange for lower processing robustness. Lastly, other hardware usagemay include two unit-less values per additional hardware resourceindicating, for example, the user's willingness to sacrifice desiredfrequency and accuracy in exchange for instructions that use fewerhardware resources of the server electronic device 901.

The method 1000 may continue with one or more computer processingdevices of the server electronic device determining measurementinstructions based on the desired operational parameters (Step 1004).For example, the server electronic device 901 may generate and provideinstructions to one or more of the client electronic devices 916, 918,920, 922, and 924 to ping one or more other client electronic devices916, 918, 920, 922, and 924, measure data (e.g., distance data betweenclient electronic devices), and report the measured and captured databack to the server electronic device 901. Specifically, in certainembodiments, the server electronic device 901 may estimate theapproximate geolocation of one or more of the client electronic devices916, 918, 920, 922, and 924 by sending instructions to at least some ofthe client electronic devices 916, 918, 920, 922, and 924 to ping,measure, and report their measurements (e.g., distance data, velocitydata, acceleration data, accelerometer data, magnetometer data,gyroscope data, GPS data, and so forth). In some embodiments, themeasurement instructions provided by the server electronic device 901may be specific to each of the client electronic devices 916, 918, 920,922, and 924 and may be based on, for example, the desired operationalparameters (e.g., frequency, accuracy, power consumption, processingcapacity, bandwidth, hardware usage, and so forth) received in therequest.

The method 1000 may continue with one or more computer processingdevices of the server electronic device transmitting the measurementinstructions to at least a subset of the client electronic devices (Step1006). For example, the server electronic device 901 may send each or asubset of the client electronic devices 916, 918, 920, 922, and 924 aninstruction message, which may include, for example, a decision-treeindicating which instruction steps should be executed and at which time.Specifically, each measureable outcome of an execution may thendetermine which instruction step or execution to perform next.

In certain embodiments, the instructions provided by the serverelectronic device 901 to the client electronic devices 916, 918, 920,922, and 924 may be structured such that when one of the clientelectronic devices 916, 918, 920, 922, and 924 is instructed to measure,then the server electronic device 901 has already calculated that aprobability threshold (e.g., high enough probability) has been achieved,thus indicating that the measurement is likely to be successful inmeasuring, for example, a physical distance, a signal intensity, apattern, an image, distance data, RF signal intensity data, latencydata, response time data, image data, audio data, voice data, biometricdata, video data, temperature data, humidity data, atmospheric data,light data, magnetic data, social media data, or other measurement datathat may improve certainty in future geolocation calculations to beperformed by the server electronic device 901.

In some embodiments, for example, the server electronic device 901 maydetermine whether the probability threshold (e.g., minimum acceptableprobability or above 50% probability, above 60% probability, above 70%probability, above 80% probability, above 90% probability) has beenachieved based on whether another client electronic device 916, 918,920, 922, and 924 has been instructed to ping, or whether thatparticular client electronic device has met a probability threshold(e.g., minimum acceptable probability or above 50% probability, above60% probability, above 70% probability, above 80% probability, above 90%probability) with respect to being nearby. The server electronic device901 may also determine whether the probability threshold (e.g., minimumacceptable probability or above 50% probability, above 60% probability,above 70% probability, above 80% probability, above 90% probability) hasbeen achieved based on whether the particular client electronic devicehas been instructed to set hardware parameters to values that have ahigh probability (e.g., probability is high enough) with respect tobeing compatible with the measurement parameters captured, for example,by the initial client electronic device.

In other embodiments, for example, the server electronic device 901 maydetermine whether the probability threshold (e.g., minimum acceptableprobability or above 50% probability, above 60%, probability, above 70%probability, above 80% probability, above 90% probability) has beenachieved based on whether there is an object with, for example, aquasi-permanent location and that is transmitting a signal, an image,audio data, voice data, biometric data, video data, temperature data,humidity data, light data, magnetic data, social media data, or othersimilar identifiable measurement data that can be detected by any of theclient electronic devices 916, 918, 920, 922, and 924 or the serverelectronic device 901 with a minimum acceptable probability. In certainembodiments, as previously noted, the client electronic devices 916,918, 920, 922, and 924 may process the outcome of each instructedexecution and refer again to the decision-tree of the instructions todetermine a manner in which to proceed. For example, in one embodiment,a pair of the client electronic devices 916, 918, 920, 922, and 924 maybe instructed to ping and measure at the same time using a set ofpossible parameters, and once the pair successfully transfers a message,the pair may be instructed to terminate and report.

In certain embodiments, the terminal action of each instructiondecision-tree, regardless of any outcomes, is either the command to“wait” for further instruction or the command to report back to theserver electronic device 901. If the client electronic device 916, 918,920, 922, and 924 is commanded to report, then the information utilizedto complete this action (e.g., which measurements to report and whatformatting to use) may be included in that command in the instructions.It should be appreciated that the measurement instruction messagesprovided to the client electronic devices 916, 918, 920, 922, and 924 bythe server electronic device 901 may include any number of instructionsor sets of instructions. For example, the instructions may be any numberof actions (e.g., measure, ping, alert, report, store, pause processingmessages for a period of time, and so forth) to be performed by one ormore of the client electronic devices 916, 918, 920, 922, and 924.

In certain embodiments, identification data may also be sent within eachtype of message. For example, identification data may be included in theinitial request from the client electronic devices 916, 918, 920, 922,and 924 to the server electronic device 901, as well as in the pingmessage sent from, for example, one or more of the client electronicdevices 916, 918, 920, 922, and 924 to one or more other clientelectronic devices 916, 918, 920, 922, and 924. In some embodiments,because each of the client electronic devices 916, 918, 920, 922, and924 that are near each other in both space and time may report to thesame server electronic device 901, each client electronic devices 916,918, 920, 922, and 924 may choose to divulge their respective data inlimited form: not at all times, or not to all parties, or not incomplete form, or not in continuous form. An example of incompletedivulgence includes the use of a hashed identification number in placeof its full unencrypted literal. An example of an non-continuous form ofdivulgence includes changing a client's identification number to a newidentification number unlinked to the previous, for example.

The method 1000 may continue with the one or more computer processingdevices of the server electronic device receiving measurement data fromat least the subset of the plurality of client electronic devices (Step1008). In certain embodiments, the server electronic device 901 mayutilize these measurements to triangulate the client electronic devices916, 918, 920, 922, and 924 (or a subset thereof) as part of the presenttechniques to determine the approximate geolocations of the clientelectronic devices 916, 918, 920, 922, and 924 within or about theenvironment 801. For example, the server electronic device 901 mayidentify one or more particular client electronic devices 916, 918, 920,922, and 924 whose current need for accuracy is higher than the currentestimated ability of the server electronic device 901 to provide such anaccurate estimate.

The server electronic device 901 may then utilize, for example, adatabase to identify a list of client electronic devices 916, 918, 920,922, and 924 and those objects (e.g., permanently positioned objects orquasi-permanently positioned objects within or about the environment801) that are nearby with a minimum acceptable probability (e.g., abovea certain percentage). For example, one or more databases of the serverelectronic device 901 may be utilized to codify the user behavior thatis likely to be performed by which client electronic devices 916, 918,920, 922, and 924 at which times for each possible location 802, forexample. In some embodiments, this information may be stored efficientlyby geographically sectioning the environment 801 and accumulatingsummary information about each section and/or each location 802.Specifically, the server electronic device 901 may utilize thelongitude, latitude, and altitude axes to section the captured andstored information and generate, for example, a collectively exhaustiveand mutually exclusive (CEME) sectioning system.

In certain embodiments, the server electronic device 901 may generatethe CEME sectioning system by forming a CEME set of latitude intervals.For example, in one embodiment, letting [0-1] be the set of alllatitudes between 0.0 and 1.0 degrees, the server electronic serverdevice 901 may generate, for example, intervals [−90,−89], [−89,−88], .. . [−1,0], [0-1], [1-2], [2-3], . . . [88-89], and [89-90] (e.g., up toapproximately 180 intervals) as a valid CEME set. Specifically, theserver electronic device 901 may form a CEME set of latitudes and form aCEME set of altitudes by, for example, executing a Cartesian crossing ofthe CEME set of latitudes and the CEME set of altitudes to form a3-dimensional (3-D) CEME sectioning of space of the environment 801, forexample. For example, in one embodiment, [0,−1], [0-1], and [0,1] mayinclude the cube-like area (which may be referred to herein as a “cube”)of space on latitudes between 0.0 and 1.0 degrees, on longitudes between0.0 and 1.0 degrees, and on altitudes between 0.0 to 1.0 meters above adeterminable altitude level. For each “cube,” the server electronicdevice 901 may create another CEME sectioning of 3-D space, furtherdividing the “cube” into smaller “cubes,” for example. In someembodiments, the server electronic device 901 may repeat this process,for example, to increase resolution over time. The server electronicdevice 901 may also utilize the CEME sectioning as an index to identifywhich databases of the server electronic device 901 are available andfor which regions are the databases available. In some embodiments, forexample when a database is added to the server electronic device 901,the added database may find all “cubes” of all sizes for which it hasinformation.

In certain embodiments, each database of the server electronic device901 may then store, for example in some sortable order, a list ofdefined 3-D enclosures and information about the enclosures. Theenclosures might include, for example, the location of certainobstructions or constructions (e.g., walls, islands, columns, pillars,and so forth) or other quasi-permanent objects that may at leastpartially skew or otherwise prevent measurement data. The informationstored by the server electronic device 901 may include, for example,which types of signals the objects obstruct, the degree of obstruction(e.g., level of obstruction), the location of “user hotspots” or otherfrequently visited spots by users, the time-of-day and day-of-week the“user hotspots” are frequented, the probability of users being at the“user hotspots” at certain times, the probability of users being at the“user hotspots” at certain times given the user, for example, was withina certain radius at another time. The information stored by the serverelectronic device 901 may also include, for example, the delay ofhardware signaling devices, formulas for calculating estimated distancefor each make and model of client electronic devices 916, 918, 920, 922,and 924, the location of, for example, pedestrian, automobile, train,and other similar pathways of the environment 801, a list of enclosedareas of the environment 801, and the distribution of observed speeds ofclient electronic devices 916, 918, 920, 922, and 924 moving throughcertain areas. The information stored by the server electronic device901 may also include, for example, functions indicating how thatprobability distribution varies over time-of-day and day-of-week,functions indicating how that distribution varies according to the makeand model of the client electronic devices 916, 918, 920, 922, and 924,and so on and so forth.

In some embodiments, the server electronic device 901 may calculate andstore certain predictions with respect to users of the client electronicdevices 916, 918, 920, 922, and 924. For example, in one embodiment,some prediction values may depend on, for example, the current weatherforecast or other similar data. Such data, for example, may be utilizedby the server electronic device 901 to learn some behavior of the userand make one or more predictions regarding, for example, thedemographics and possible locations of the user. In another embodiment,the user may voluntarily input some additional personal or observedinformation utilizing, for example, one or more applications running onthe client electronic devices 916, 918, 920, 922, and 924 such as theapplication components 926, 928, 930, 932, and 934 previously discussed.Such data may be received and stored by the server electronic device 901and may be utilized, in some embodiments, in determining present orfuture measurement instructions to provide to the client electronicdevices 916, 918, 920, 922, and 924.

In some embodiments, as part of a delivery message, the serverelectronic device 901 may also calculate the maximum probable geographiclocation of the message source and destination. Thus, for a message m,the server electronic device 901 may calculate the most likely points xand y that the message m may be sent and received. The locationestimations x and y may be constantly updated by the server electronicdevice 901. After some amount of time after each message m is reported,the server electronic device 901 may generate an estimate for theapproximate geolocation of that message's endpoint locations x and y.That message m may include a hardware type, and may thus include animplied distance d, which as will be further appreciated below withrespect to Step 1010 of FIG. 10, may be utilized by the serverelectronic device 901 to infer distance (e.g., distance with respect tothe client electronic devices 916, 918, 920, 922, and 924) as part ofthe geolocation estimation calculation.

In certain embodiments, the server electronic device 901 may alsoutilize a CEME set of intervals in location, velocity, and othersuitable or desirable information to store a distribution of observedvelocities. For example, a distribution may be based on device ID orcertain other distributions may be based on location (e.g., “cubes” andenclosures). Angular velocity and acceleration may be estimated, forexample, for every three consecutive geolocation estimates. The serverelectronic device 901 may weigh recent information as more indicative ofpresent outcomes by utilizing, for example, one or more supervisedlearning and cross-validation tuning techniques. In this way, the serverelectronic device 901 may predict behavior and possible measurement orsignal outcomes. The server electronic device 901 may also generate andprovide different levels of confidence for the probable estimations, aswill be appreciated in greater detail with respect to Step 1012 of FIG.10.

For example, in some embodiments, the server electronic device 901 mayinitially observe that a user ID associated with a particular one of theclient electronic devices 916, 918, 920, 922, and 924 spends time evenlybetween two locations. However, once unsupervised analysis is performed,the server electronic device 901 may determine that the distribution oflocations varies significantly between, for example, before noon (e.g.,morning) and afternoon (e.g., midday to late evening) each day. Thus,the server electronic device 901 may henceforth also store the morningand afternoon distributions for that particular user ID and clientelectronic device, for example. Furthermore, as contiguous “cubes” areidentified as having similar properties (for one or more data types),the server electronic device may define a new enclosure. For example,large pathways such as large stretches of major highways may be groupedinto enclosures to represent, for example, the distribution of trafficspeed observed in each lane and at what time of day and day-of-week. Insuch a case, the server electronic device 901 may utilize unsupervisedlearning or clustering to determine which contiguous “cubes” to utilizeto make a new enclosure. Specifically, in accordance with the presenttechniques, the server electronic device 901 may accumulate behavioralmaps (e.g., user specific patterns) such as road maps, pedestrianwalkways, building maps, air density maps, signal interference maps, andthe like.

For example, in the example in which a company adds a new building toits campus (e.g., environment 801) and the client electronic devices916, 918, 920, 922, and 924 send requests while corresponding users walkthroughout the new campus building, the server electronic device 901 mayincrementally update its determination about each square foot of thearea to reflect (e.g., based on fuzzy logic) a sense of the location of,for example, the roads, walls, furniture areas, islands, columns,frequent walk-paths, metal-plates, the location and basic shape ofhighly unique images, and favorite work spots, walking speeds of thecompany employees, frequent visitors, and so forth with respect to thenew campus building. The server electronic device 901 may then utilizethis information to predict the probable location and velocity ofcurrent visitors and the probable distance of a measurement readingbased upon the historical behavior and signal interference of previousobservations, for example. As another example, if users of the clientelectronic devices 916, 918, 920, 922, and 924 are walking past thefront door of the new campus building and, for example, the clientdevices include a magnetic imaging measurement device, the serverelectronic device 901 may detect a strong signal to the users' East, forexample, over time. The server electronic device 901 may utilize thatadditional real-time information to further triangulate the approximategeolocation of a client electronic device 916, 918, 920, 922, and 924currently requesting a geolocation estimate, for example.

In certain embodiments, when one or more of the client electronicdevices 916, 918, 920, 922, and 924 reports its findings, the serverelectronic device 901 may utilize the information to validate or correctthe previous expectations or assumptions that formed the foundation ofprior decisions (e.g., determine with certainty whether the serverelectronic device 901 now have enough information to meet the userpreferences, whether the client electronic devices and objects that theserver electronic device 901 calculated were nearby with highprobability are indeed nearby, whether additional client electronicdevices and objects have moved or remain stationary, whether additionaluser preferences have been received by the server electronic device 901,etc.).

The method 1000 may continue with the one or more computer processingdevices of the server electronic device generating geolocationestimation data for the one or more client electronic devices (Step1010). For example, in certain embodiments, the server electronic device901 may estimate the approximate geolocation of the client electronicdevices 916, 918, 920, 922, and 924 by utilizing, for example,supervised learning on the probable location of a device ID based uponits historical distribution, the probable distances between the clientelectronic devices 916, 918, 920, 922, and 924, based on the measurementdata, the probable distance between each of the client electronicdevices 916, 918, 920, 922, and 924 and known obstructions (e.g., walls,magnetic fields, object images, and so forth), and the probablevelocities and accelerations of the client electronic devices 916, 918,920, 922, and 924 based upon, for example, the historical distributionof the device ID and the location historical distribution of theenvironment 801.

In certain embodiments, for each delivery message, the server electronicdevice 901 may determine an estimated location, x=(lat, lon, alt), aradius=r, and a probability of one or more of the client electronicdevices 916, 918, 920, 922, and 924 being within that distance of theestimate=p. For each client electronic device 916, 918, 920, 922, and924 respective device ID i, and for each “cube” of environment 801space, the database of the server electronic device 901 may keep trackof a cumulative score by, for example, adding “points” at each iterationto a running tally. In one embodiment, for some monotonically increasingfunctions ƒ and g, whenever the server electronic device 901 sends adelivery message with (x, r, p), then

${{{points}(y)} = \frac{f\left( {p,r^{- 1}} \right)}{g\left( {{distance}\left( {x,y} \right)} \right)}},$

with distance (x, y) defined as the average Euclidean distance between xand the set of points that constitute “cube” y. After each deliverymessage, the server electronic device 901 may cumulate the points ateach “cube” by computing, for example, cumulative points(y)=points(y).Thus, at any moment in time, for any CEME set of “cubes” C, the serverelectronic device 901 may estimate the prior probability that aparticular client electronic device 916, 918, 920, 922, and 924 islocated in any “cube” y (that belongs to C) as:

${{prior}\left( {i,y} \right)} = \frac{{cumulative}\mspace{14mu} {{points}(y)}}{\sum_{z \in C}{{cumulative}\mspace{14mu} {{points}(z)}}}$

In another embodiment, the server electronic device 901 may estimate theconditional probability that a particular client electronic device 916,918, 920, 922, and 924 is located in “cube” y (given that it is locatedin a larger “cube” Y), where there exists a set of CEME “cubes” C, suchthat Y=Union(C), and wherein y enclosed fully by Y:

${{conditional}\left( {i,{yY}} \right)} = \frac{{cumulative}\mspace{14mu} {{points}(y)}}{\sum_{z \in {{CEME}{(Y)}}}{{cumulative}\mspace{14mu} {{points}(z)}}}$

In certain embodiments, to ensure that these probabilities match arealistic determination, for example, the server electronic device 901may initialize the cumulative points of each cube with third-party dataabout the human density of the environment 801. The server electronicdevice 901 may also, in some embodiments, utilize a Bayesian smoothingtechnique to ensure that these probabilities match a realisticdetermination. For example, the server electronic device 901 mayinitialize the cumulative points of each “cube” with some small valueproportional to its volume, as illustrated by the equation set forthbelow:

cumulative points at first instance of ID=h(volume(y)),

-   -   for some monotonically Increasing h

In another embodiment, to ensure that these probabilities match arealistic determination, the server electronic device 901 may generateand assign a penalty to, for example, the assumption or the expectationthat a particular client electronic device 916, 918, 920, 922, and 924is in a particular location, as set forth by the equation below:

Location loss(y,i)=ƒ(prior(y,i))

-   -   for some monotonically decreasing function, ƒ

In certain embodiments, as previously discussed above, whenever ageolocation estimate is completed, the server electronic device 901 maycalculate a residual by comparing the distance implied by thegeolocation estimate and the raw estimated distance. For example, theresiduals may include a constant stream of supervised feedback uponwhich, for example, a sophisticated model may be generated by the serverelectronic device 901. For example, in certain embodiments, the serverelectronic device 901 may generate a sophisticated model that may learnmultiplicative, linear, and complex biases based on the availablecontext information (e.g., the requesting device ID, the pinging deviceID, the time of day, the weather, the hardware settings, the make andmodel of the hardware, and so forth). For example, in or moreembodiments, the server electronic device 901 may cause the model to betrained online, and may thus provide access to a context-bias adjustedestimated (CBAE) distance. The server electronic device 901 may theninput the raw estimated distance and the known and available contextinformation. The generated model may then return a prediction that isequal to, for example, the CBAE distance.

In some embodiments, for any two locations x and y (e.g., which may berepresented as vectors in some embodiments), the server electronicdevice 901 may determine an actual distance equal to the Euclideandistance between the two locations x and y. For example, as previouslydiscussed above, for each possible message type and for each possiblelocation, the server electronic device 901 may store (e.g., utilizing“cubes” and/or enclosures) the observed bias between the implieddistance of the final geolocation estimation and the implied distance ofthe measurement data. Integrating these known biases from location x tolocation y, the server electronic device 901 may calculate thelocation-bias adjusted estimated (LBAE) distance. Thus, for any twopoints (x, y) and any measurement m, the difference between LBAE(x, y)and CBAE(m) may include the implied residual or error in measurement m,given that the measuring client electronic device and pinging clientelectronic device are located at locations x and y. In some embodiments,in order to discover which assumed geolocations of the client electronicdevices 916, 918, 920, 922, and 924 minimize measurement loss, theserver electronic device 901 may define an error function as set forthbelow:

measurement loss(x,y,m)=h(|CBAE(m)−LBAE(x,y)|·CBAE(m))

-   -   for some h(⋅,⋅), monotonically increasing in the first argument

For example, in some embodiments, the server electronic device 901 maydefine an error penalty associated with each of the possible twolocations x and y, for example, by comparing the measurement expectedbased on both the known available context (e.g., device make-model,device ID, weather, time, and so forth) and the historical biasesobserved in the “cubes” and enclosures in between locations x and y tothe implied distance measurement. In the above error function, thedifference calculation is proportional to the error calculation.

In certain embodiments, the client electronic devices 916, 918, 920,922, and 924 may be utilized to process complex messages when attemptingto measure, for example, an object. For example, the client electronicdevices 916, 918, 920, 922, and 924 may each include a camera or otherdata capturing device, which may, at least in some embodiments, receivea 2-D image array corresponding to the captured image of the measuredobject. In certain embodiments, in order to assimilate these messagesinto the server electronic device 901, the server electronic device 901may rely upon, for example, the possibility that all messages (e.g.,both relatively simple and more complex messages) may be compared toother messages of the same type and saved in the database of the serverelectronic device 901. Specifically, for each hardware type, the serverelectronic device 901 may determine a similarity function that comparesthe received message with the average message received at a knownlocation. Thus, the server electronic device 901 may rely upon eachhardware type having a similarity-score function that compares themessage information m with any retrieved message from the database i, asillustrated by the following equation:

similarity_(h)(m,i)

-   -   for each hardware type h    -   with range=[0,1]

In certain embodiments, for each hardware type, message, and comparisonmessage, the client electronic devices 916, 918, 920, 922, and 924 mayestimate distance between, for example, the camera and the image underthe assumption that the measurement data includes the comparison object.For example, given the aperture and focal length of a camera of theclient electronic devices 916, 918, 920, 922, and 924, and the knownsize of an object, the client electronic devices 916, 918, 920, 922, and924 may estimate the distance between the object and the camera by itsrelative size inside the image. In some embodiments, the serverelectronic device 901 may rely on each measuring hardware type (e.g.,camera or other data capturing device) having a distance estimationOE_(hi)(m) (e.g., object estimated [OE] distance) for each hardware typeh and each assumed object i. In one or more embodiments, the serverelectronic device 901 may define measurement loss in this case as:

measurementloss(x,i,m)=min(ƒ(similarity_(h)(m,i))·_(g)(|OE_(h|i)(m)−LBAE(x,y)|,OE_(h|i)(m)))

-   -   where y=the location of i    -   where h is the hardware of m    -   for some monotonically increasing functions ƒ and g

For example, in certain embodiments, as may be ascertained by the aboveequation, the server electronic device 901 may define an error penaltyassociated with each possible client electronic device 916, 918, 920,922, and 924 location x and by comparing the measurement expected basedon the hardware formula and the historical biases observed in the“cubes” and enclosures between location x and the known location of theobject y. In some embodiments, possible types of error may include, forexample, the possibility that an object of interest was not the actualobject included in the measurement data, and the possibility that thedistance calculation between the camera or other data capturing deviceof the client electronic device 916, 918, 920, 922, and 924 and theobject is at least partially inaccurate.

Thus, the server electronic device 901 may determine a compromisebetween the two possible errors by computing the error (e.g., aunit-less value) of each, and then calculating and utilizing the minimumof the two possible errors. In some embodiments, if the serverelectronic device 901 determines that the first possible error type isless than the other possible error type, then the server electronicdevice 901 may determine that the overall error is monotonicallyincreasing in the similarity score returned. Specifically, the serverelectronic device 901 may determine that the penalty of rejecting theimage or other captured measurement data as correct is high when thesimilarity is high. On the other hand, if the server electronic device901 determines that the second error possible type is less than thefirst possible error type, then the server electronic device 901 maydetermine that the overall error decreases when the estimated distancefrom the objection information calculation OE is closer to the locationadjusted distance between the two locations x and y and increases whenthese two values diverge.

In certain embodiments, the server electronic device 901 may alsoestimate the velocities and accelerations of the client electronicdevices 916, 918, 920, 922, and 924 at various times. Specifically,another type of error is the error incurred by assuming that aparticular client electronic device 916, 918, 920, 922, and 924 is atposition x_(t1), and at time t₁ and at another position x_(t2) at timet₂. For example, the server electronic device 901 may determine thatthere should be a large error incurred if x_(t1) and x_(t2) are farenough apart that (t₂−t₁) and the historical velocity of the particularclient electronic device 916, 918, 920, 922, and 924 and the historicalvelocity at or near this location. Specifically, for each consecutivetime t_(i−1), t₁, and t_(1+i), the server electronic device 901 maycalculate velocity and acceleration error of, for example, particularclient electronic device 916, 918, 920, 922, and 924 as:

user velocity loss(v _(i) ,v _(i+1))=ƒ(p(v _(i) ,v _(i+1)|user ID=x ₁))

-   -   for some decreasing function ƒ

Similarly, the server electronic device 901 may calculate velocity andacceleration error of, for example, particular client electronic device916, 918, 920, 922, and 924 as:

locution velocity loss(v _(i) ,v _(i+1))=ƒ(p(v _(i) ,v _(i+1)|location=x₁))

-   -   for some decreasing function ƒ

In certain embodiments, the server electronic device 901 may calculatespecific geolocation estimates of any of the client electronic device916, 918, 920, 922, and 924. For example, in certain embodiments, if thecurrent time is T, then the server electronic device 901 may considerall measurements by all devices in the last delta(t) minutes, whichdelta (t) is a global lookback parameter of the sever electronic device901. For example, the number of measurements may be finite, then thereis a finite set of times in [T−delta(t), T]. Having retrieved the set ofall measurements in the lookback window, and having the list of allsending client electronic devices 916, 918, 920, 922, and 924 and allpossible pinging device IDs of client electronic devices 916, 918, 920,922, and 924, the server electronic device 901 may generate ageolocation estimate by solving for the estimated geolocations of eachof the client electronic devices 916, 918, 920, 922, and 924concurrently.

In some embodiments, the server electronic device 901 may also minimize,for example, the total error over all sets of possible environment 801positions of each client electronic devices 916, 918, 920, 922, and 924.Specifically, in some embodiments, the server electronic device 901 maydeclare the position of each connection ID x €X at each time t€{T},x_(t) to be a variable. The server electronic device 901 may thenutilize, for example, a non-linear optimization technique to calculate asolution that minimizes the total error objective function (e.g.,corresponding to a function of those variables). The server electronicdevice 901 may keep track of all connection IDs used within thislookback regardless of whether they are currently active connections ornot. In some embodiments, another variable in the total error functionis the probability that a particular client electronic device 916, 918,920, 922, and 924 with connection ID c and device ID i, is the actualpinger for message m (e.g., for each message m) in the current lookbackwindow of the type that may include a pinging device pinger(m, c). Inone embodiment, the server electronic device 901 may restrict pinger(m,c) to only non-negative values (e.g., non-negative integers) to ensurethat pinger(m, c) may include a valid set of probabilities (e.g.,pinger(m, c)>0).

In certain embodiments, to account for the possibility that an unknownoccurrence caused the appearance of a ping, the server electronic device901 may calculate and assign a pinger loss penalty, as set forth below:

pinger loss(m)=ƒ_(h)(1−Σ_(c)pinger(m,c))

-   -   for some monotonically increasing function, ƒ_(h)    -   where h is the hardware of m

As may be ascertained from the above equation, 1−Σ_(c)pinger(m, c) mayrefer to the probability that an unknown occurrence occurred, and theserver electronic device 901 may assign an increasing penalty based onthe unknown occurrence. In certain embodiments, the penalty may varyaccording to hardware type, as some types of hardware components may bemore susceptible to such an occurrence. Further, in some embodiments, ifa particular client electronic device 916, 918, 920, 922, and 924 isknown not to have the hardware necessary to perform a ping message, thenthat device may not considered: (pinger(m, c)=0). Additionally, if amessage is of a hardware type that includes a particular pinging clientelectronic device 916, 918, 920, 922, and 924 being instructed to ping,then the client electronic devices 916, 918, 920, 922, and 924 that donot meet a defined criteria may not be considered.

For example, in one embodiment, the defined criteria may be based onwhether the particular pinging client electronic device 916, 918, 920,922, and 924, for example, received an instruction to ping on acompatible hardware setting before the message timestamp within alookback horizon. In another embodiment, the defined criteria may bebased on whether either the last report received by the particularpinging client electronic device 916, 918, 920, 922, and 924 was beforethe instruction, or whether, for example, the first report received bythe server electronic device 901 after the instruction was also afterthe message timestamp. The defined criteria may thus prevent any clientelectronic device that could not have been the particular pinging clientelectronic device 916, 918, 920, 922, and 924 from being attributed anyprobability density in the solution to the total-loss function. Anothervariable in the total error function may include the probability thatobject o was the measured object for message m (e.g., for each message min the current lookback window) of the type that include an objectplacement (m, o). In one embodiment, the server electronic device 901may restrict placement (m, o) to only non-negative values (e.g.,non-negative integers) to ensure that placement (m, o) may include avalid set of probabilities (e.g., placement(m, o)>0).

In certain embodiments, to account for the possibility that an unknownoccurrence caused the appearance of a ping, the server electronic device901 may calculate and assign a placement loss penalty, as set forthbelow:

placement loss=ƒ_(h)(1−Σ_(o)placement(m,o))

-   -   for some monotonically increasing function, ƒ_(h)    -   where h is the hardware of m

In certain embodiments, as may be ascertained from the above equation,1−Σ₀ placement(m, o) may refer to the probability that an unknownoccurrence occurred, and the server electronic device 901 may assign anincreasing penalty based on the unknown occurrence. As discussed abovewith respect to the pinger(m, c), in certain embodiments, the penaltymay vary according to hardware type, as some types of hardwarecomponents may be more susceptible to such an occurrence. Further, insome embodiments, if an object is known not to have the propertiesnecessary, then that object is not considered: (placement(m, o)=0). Thismay thus prevent any object that could not have been the detected objectfrom being attributed any probability density in the solution to thetotal-loss function calculated by the server electronic device 901.

In certain embodiments, with the variables X, pinger (m, c), andplacement(m, o) defined, the server electronic device 901 may calculatethe total loss as:

total loss=Σ_(t∈T)loss_(t)

loss_(t)=pinger(m _(t) ,c)*measurement loss(x _(t) ^(c) ,x _(t) ^(s) ,m_(t))+ . . . +Σ_(o)placement(m _(t) ,o)*measurement loss(y ^(i) ,x _(t)^(s) , m _(t))+ . . . +Σ_(c)location loss(i ^(c) ,x _(t) ^(c))+ . . .+user velocity loss(v _(t-1) ,v _(t))+ . . . +location velocity loss(v_(t-1) ,v _(t))+ . . . +pinger loss(m _(t))

where the sender of message m, is s, and is known

-   -   where the device ID of device with connection ID=c is i^(c), and        is known    -   where velocity losses are zero when t−1∉T

Thus, the server electronic device 901 may calculate the approximategeolocation of each of the client electronic devices 916, 918, 920, 922,and 924. In one embodiment, the server electronic device 901 mayminimize the above function subject to the following constraints:

pinger(m _(t) ,c)≥0

placement(m _(t) ,o)≥0

Σ_(o)placement(m,o)≤1

Σ_(o)pinger(m, o)≤1

-   -   plager(m_(t), c)=0, when infeasible    -   placement(m_(t),o)=0, when Infeasible

The method 1000 may continue with the one or more computer processingdevices of the server electronic device generating a geolocationconfidence value (Step 1012). Specifically, the geolocation estimate mayconstitute only part of the delivery message. In certain embodiments,the server electronic device 901 may also calculate a geolocationconfidence level of the geolocation estimate. In one embodiment, theserver electronic device 901 may convey confidence as a confidenceinterval. For example, a distance d and a probability p combinedtogether by the server electronic device 901 may indicate that thegeolocation estimate is accurate enough that p percent of similargeolocation estimates are within d distance of the actual geolocation ofthe client electronic devices 916, 918, 920, 922, and 924.

In some embodiments, the server electronic device 901 may calculate thegeolocation confidence level by monitoring, for example, the gradient ofthe total loss function. For example, the gradient of a function mayinclude the rate at which the value changes as one of its variables ismoved or varied. Specifically, when the gradient of the total lossfunction is small, the server electronic device 901 may determine thatpoints nearby the estimated geolocation are only slightly less likelythan the geolocation estimate itself. In such a case, the serverelectronic device 901 may determine a low level of confidence. In someembodiments, the server electronic device 901 may link what has beendetermined from the gradient with what is desired, for example, by auser, and generate a confidence interval or other form of confidencereport. The below equation illustrates the foregoing:

$p_{x} = \left( {{\frac{\partial}{\partial x}\mspace{14mu} {Total}\mspace{14mu} {Loss}},d_{x}} \right)$where  d_(x)  is  the  requested  delivery  radius  of  device  xfor  some  function  g, monotonically  increasing  in  the  first  argument  anddecreasing  in  the  second

For example, referring to the equation above, the function g may beimproved by selecting the best functions from a family of functions G.The server electronic device 901 may determine which function is bestby, for example, adding the client electronic devices 916, 918, 920,922, and 924 at known locations and ranking possible candidate functiong, or, in another embodiment, instructing the client electronic devices916, 918, 920, 922, and 924 with known locations to rank possiblecandidate function g.

The method 1000 may then conclude with the one or more computerprocessing devices of the server electronic device transmitting thegeolocation estimation data and the geolocation confidence value to oneor more of the client electronic devices 916, 918, 920, 922, and 924(Step 1014) indicating, for example, the approximate geolocation of eachof the client electronic devices 916, 918, 920, 922, and 924 within theenvironment 801 and a geolocation confidence value indicating, forexample, the confidence level that the geolocation estimation is theactual geolocation of each of the client electronic devices 916, 918,920, 922, and 924. In this way, the present embodiments may providetechniques to efficiently determine and track the approximategeolocation of electronic devices within or about indoor and outdoorenvironments, or otherwise in any of various environments in whichlarge-scale satellite systems may be inaccurate, imprecise, or otherwiseunavailable.

Turning now to FIG. 11, which illustrates is a flow diagram of a method1100 of determining and tracking the approximate geolocation devices,and, more specifically, of calculating geolocation estimation data forthe electronic devices in accordance with the present embodiments.Similarly as discussed above with respect to the method 1000 of FIG. 10,the method 1100 may also be performed by processing logic that mayinclude hardware such as one or more computer processing devices,software (e.g., instructions running/executing on a computer processingdevice), firmware (e.g., microcode), or a combination thereof, such asthe server electronic device 901 discussed above with respect to FIG. 9.

The method 1100 may begin with one or more computer processing devicesof the server electronic device identifying one or more clientelectronic devices of a plurality of client electronic devices to belocated (Step 1102). The method 1100 may continue with one or morecomputer processing devices of the server electronic device generatinggeolocation estimation data and a geolocation confidence value withrespect to the one or more client electronic devices based on, forexample, at least one geolocation estimation model (Step 1104). Forexample, as previously discussed above with respect to method 1000 ofFIG. 10, the server electronic device 901 may determine an approximatephysical location of one or more client electronic devices 916, 918,920, 922, and 924 based on a device ID of the one or more clientelectronic devices 916, 918, 920, 922, and 924 in accordance with ahistorical distribution, based on approximate distances measured betweeneach of a number the client electronic devices 916, 918, 920, 922, and924 with respect to each other or with respect to one or more knownphysical obstructions or constructions (e.g., walls, islands, columns,pillars, and so forth), or based on an estimated velocity and anacceleration of the client electronic devices 916, 918, 920, 922, and924.

The method 1100 may continue with one or more computer processingdevices of the server electronic device determining an approximatephysical location of the client electronic device based on thecalculated geolocation estimation data and the calculated geolocationconfidence value (Step 1106) (e.g., as generally discussed above withrespect to FIG. 10). The method 1100 may then conclude with one or morecomputer processing devices of the server electronic device storing thegeolocation estimation data and the geolocation confidence value (Step1108).

Turning now to FIG. 12, which illustrates is a flow diagram of a method1200 of determining and tracking the approximate geolocation ofelectronic devices, and, more specifically, a method for receivingcalculated geolocation estimation data at a client electronic device inaccordance with the present embodiments. The method 1200 may beperformed by processing logic that may include hardware such as one ormore computer processing devices, software (e.g., instructionsrunning/executing on a computer processing device), firmware (e.g.,microcode), or a combination thereof, such as the server electronicdevice 901 discussed above with respect to FIG. 9.

The method 1200 may begin with one or more processing devices of aclient electronic device receiving desired operational parameters on theclient electronic device (Step 1202). The method 1200 may continue withone or more computer processing devices of a client electronic devicetransmitting the desired operational parameters to a server electronicdevice (Step 1204) (e.g., as generally discussed above with respect toFIG. 10). The method 1200 may then continue with one or more computerprocessing devices of a client electronic device receiving measurementinstructions from the server electronic device based on the desiredoperational parameters (Step 1206). The method 1200 may then continuewith one or more computer processing devices of a client electronicdevice transmitting the measurement instructions to at least a subset ofa plurality of other client electronic devices (Step 1208) (e.g., asgenerally discussed above with respect to FIG. 10).

The method 1200 may then continue with one or more computer processingdevices of a client electronic device generating measurement data basedon the measurement instructions received from the server electronicdevice (Step 1210). The method 1200 may then continue with one or morecomputer processing devices of a client electronic device transmittingthe measurement data to the server electronic device (Step 1212). Themethod 1200 may then conclude with one or more computer processingdevices of a client electronic device receiving geolocation estimationdata and a geolocation confidence value from the server electronicdevice in response to the measurement data (Step 1214) (e.g., asgenerally discussed above with respect to FIG. 10). As previously noted,the present embodiments may provide techniques to efficiently determineand track the approximate geolocation of electronic devices withinindoor environments, outdoor environment, or otherwise in any of variousenvironments in which, for example, large-scale satellite systems suchas, GPS may be inaccurate or imprecise.

FIG. 13 is a block diagram of an example computing device 1300 that mayperform one or more of the operations described herein, in accordancewith the present embodiments. The computing device 1300 may be connectedto other computing devices in a LAN, an intranet, an extranet, and/orthe Internet. The computing device 1300 may operate in the capacity of aserver machine in client-server network environment or in the capacityof a client in a peer-to-peer network environment. The computing device1300 may be provided by a personal computer (PC), a set-top box (STB), aserver, a network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlecomputing device 1300 is illustrated, the term “computing device” shallalso be taken to include any collection of computing devices thatindividually or jointly execute a set (or multiple sets) of instructionsto perform the methods discussed herein.

The example computing device 1300 may include a computer processingdevice (e.g., a general purpose processor, ASIC, etc.) 1302, a mainmemory 1304, a static memory 506 (e.g., flash memory and a data storagedevice 1308), which may communicate with each other via a bus 1310. Thecomputer processing device 1302 may be provided by one or moregeneral-purpose processing devices such as a microprocessor, centralprocessing unit, or the like. In an illustrative example, computerprocessing device 1302 may comprise a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction word (VLIW) microprocessor, or aprocessor implementing other instruction sets or processors implementinga combination of instruction sets. The computer processing device 1302may also comprise one or more special-purpose processing devices such asan application specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. The computer processing device 1302 may be configured toexecute the operations described herein, in accordance with one or moreaspects of the present disclosure, for performing the operations andsteps discussed herein.

The computing device 1300 may further include a network interface device1312, which may communicate with a network 1314. The data storage device1308 may include a machine-readable storage medium 1316 on which may bestored one or more sets of instructions, e.g., instructions for carryingout the operations described herein, in accordance with one or moreaspects of the present disclosure. Instructions implementing module 1318may also reside, completely or at least partially, within main memory1304 and/or within computer processing device 1302 during executionthereof by the computing device 1300, main memory 1304 and computerprocessing device 1302 also constituting computer-readable media. Theinstructions may further be transmitted or received over the network1314 via the network interface device 1312.

While machine-readable storage medium 1316 is shown in an illustrativeexample to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database and/or associated cachesand servers) that store the one or more sets of instructions. The term“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform the methods described herein. The term “computer-readablestorage medium” shall accordingly be taken to include, but not belimited to, solid-state memories, optical media and magnetic media.

Embodiments of the subject matter and the operations described in thisspecification can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Embodiments of the subject matterdescribed in this specification can be implemented as one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on computer storage medium for execution by, or tocontrol the operation of, data processing apparatus. Alternatively, orin addition, the program instructions can be encoded on anartificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described in this specification can be implemented asoperations performed by a data processing apparatus on data stored onone or more computer-readable storage devices or received from othersources.

The term “computer processing device” encompasses all kinds ofapparatus, devices, and machines for processing data, including by wayof example a programmable processor, a computer, a system on a chip, ormultiple ones, or combinations, of the foregoing. Although referred toas a computer processing device, use of the term also encompassesembodiments that include one or more computer processing devices. Thecomputer processing device can include special purpose logic circuitry,e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit). The computer processingdevice can also include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The computer processing device and execution environment can realizevarious different computing model infrastructures, such as web services,distributed computing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative,procedural, or functional languages, and it can be deployed in any form,including as a stand-alone program or as a module, component,subroutine, object, or other unit suitable for use in a computingenvironment. A computer program may, but need not, correspond to a filein a file system. A program can be stored in a portion of a file thatholds other programs or data (e.g., one or more scripts stored in amarkup language resource), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub-programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processing devices suitable for the execution of a computer programinclude, by way of example, both general and special purposemicroprocessors, and any one or more processors of any kind of digitalcomputer. Generally, a processing device will receive instructions anddata from a read-only memory or a random access memory or both. Theessential elements of a computer are a processor for performing actionsin accordance with instructions and one or more memory devices forstoring instructions and data. Generally, a computer will also include,or be operatively coupled to receive data from or transfer data to, orboth, one or more mass storage devices for storing data, e.g., magneticdisks, magneto-optical disks, optical disks, or solid state drives.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a smart phone, a mobile audio orvideo player, a game console, a Global Positioning System (GPS)receiver, or a portable storage device (e.g., a universal serial bus(USB) flash drive), to name just a few. Devices suitable for storingcomputer program instructions and data include all forms of non-volatilememory, media and memory devices, including, by way of example,semiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices; magnetic disks, e.g., internal hard disks or removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The processingdevice and the memory can be supplemented by, or incorporated in,special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse, a trackball, a touchpad,or a stylus, by which the user can provide input to the computer. Otherkinds of devices can be used to provide for interaction with a user aswell; for example, feedback provided to the user can be any form ofsensory feedback, e.g., visual feedback, auditory feedback, or tactilefeedback; and input from the user can be received in any form, includingacoustic, speech, or tactile input. In addition, a computer can interactwith a user by sending resources to and receiving resources from adevice that is used by the user; for example, by sending web pages to aweb browser on a user's client device in response to requests receivedfrom the web browser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back-end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back-end, middleware, or front-end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data (e.g., an HTML page) to a clientdevice (e.g., for purposes of displaying data to and receiving userinput from a user interacting with the client device). Data generated atthe client device (e.g., a result of the user interaction) can bereceived from the client device at the server.

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs can be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular inventions.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

What is claimed is:
 1. A method, comprising: obtaining measurement data from each of at least a subset of a plurality of client devices; determining, by a computer processing device, geolocation estimation data for one or more of the subset of the plurality of client devices based at least in part on the measurement data; determining, by the computer processing device, a geolocation confidence value based at least in part on the geolocation estimation data, wherein the geolocation confidence value indicates a level of confidence of the geolocation estimation data; and providing the geolocation estimation data and the geolocation confidence value to the one or more of the subset of the plurality of client devices.
 2. The method of claim 1, wherein the measurement data is generated by each respective client device of the subset of the plurality of client devices.
 3. The method of claim 1, wherein the measurement data comprises at least one of distance data, radio frequency signal intensity data, or latency data.
 4. The method of claim 1, wherein the geolocation estimation data comprises a longitude coordinate, a latitude coordinate, and an altitude coordinate, and wherein the longitude coordinate, the latitude coordinate, and the altitude coordinate indicate an approximate location of at least another one of the subset of the plurality of client devices.
 5. The method of claim 1, wherein the geolocation estimation data comprises a radial estimation, and wherein the radial estimation indicates a radius in which at least another one of the subset of the plurality of client devices resides.
 6. The method of claim 1, wherein the geolocation estimation data comprises an indication of whether a client device of the plurality of client devices desiring to be located is stationary or in motion.
 7. The method of claim 1, comprising: obtaining a desired operational parameter from the one or more of the subset of the plurality of client devices; and determining measurement instructions based at least in part on the desired operational parameter.
 8. The method of claim 7, comprising: transmitting the measurement instructions to the one or more of the subset of the plurality of client devices.
 9. The method of claim 1, comprising: determining an approximate physical location of the one or more of the subset of the plurality of client devices based on the geolocation estimation data and the geolocation confidence value.
 10. The method of claim 1, wherein the geolocation estimation data and the geolocation confidence value are determined with respect to the one or more of the subset of the plurality of client devices based at least in part on one or more geolocation estimation models.
 11. An apparatus, comprising: a computer processing device, the computer processing device to: obtain measurement data from each of at least a subset of a plurality of client devices; determine geolocation estimation data for one or more of the subset of the plurality of client devices based at least in part on the measurement data; determine a geolocation confidence value based at least in part on the geolocation estimation data, wherein the geolocation confidence value indicates a level of confidence of the geolocation estimation data; and provide the geolocation estimation data and the geolocation confidence value to the one or more of the subset of the plurality of client devices.
 12. The apparatus of claim 11, wherein the measurement data is generated by each respective client device of the subset of the plurality of client devices.
 13. The apparatus of claim 11, wherein the measurement data comprises at least one of distance data, radio frequency signal intensity data, or latency data.
 14. The apparatus of claim 11, wherein the geolocation estimation data comprises a longitude coordinate, a latitude coordinate, and an altitude coordinate, and wherein the longitude coordinate, the latitude coordinate, and the altitude coordinate indicate an approximate location of at least another one of the subset of the plurality of client devices.
 15. The apparatus of claim 11, wherein the geolocation estimation data comprises a radial estimation, and wherein the radial estimation indicates a radius in which at least another one of the subset of the plurality of client devices resides.
 16. The apparatus of claim 11, wherein the geolocation estimation data comprises an indication of whether a client device of the plurality of client devices desiring to be located is stationary or in motion.
 17. The apparatus of claim 11, wherein the computer processing device is further to: obtain a desired operational parameter from the one or more of the subset of the plurality of client devices; and determine measurement instructions based at least in part on the desired operational parameter.
 18. The apparatus of claim 17, wherein the computer processing device is further to: transmit the measurement instructions to the one or more of the subset of the plurality of client devices.
 19. The apparatus of claim 11, wherein the computer processing device is further to: determine an approximate physical location of the one or more of the subset of the plurality of client devices based on the geolocation estimation data and the geolocation confidence value.
 20. A non-transitory computer-readable medium having instruction stored thereon that, when executed by a computer processing device, cause the computer processing device to: obtain measurement data from each of at least a subset of a plurality of client devices; determine, by the computer processing device, geolocation estimation data for one or more of the subset of the plurality of client devices based at least in part on the measurement data; determine a geolocation confidence value based at least in part on the geolocation estimation data, wherein the geolocation confidence value indicates a level of confidence of the geolocation estimation data; and provide the geolocation estimation data and the geolocation confidence value to the one or more of the subset of the plurality of client devices. 